OncologyNCT06463977
Using Surveys to Examine the Association of Exposure to ML Mortality Risk Predictions With Medical Oncologists' Prognostic Accuracy and Decision-making
An observational study of Oncology, run by Abramson Cancer Center at Penn Medicine at 1 site in United States.
Trial summary
Nearly half of cancer patients in the US will receive care that is inconsistent with their wishes prior to death. Early advanced care planning (ACP) and palliative care improve goal-concordant care and symptoms and reduce unnecessary utilization. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Oncology
- What is being tested?
- Survey
- How many people
- 52 participants
- Where
- 1 siteUnited States
- Study dates
- 13 Mar 2023to 31 Dec 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: The study population consisted of a convenience sample of practicing medical oncologists who treated lung cancer in the US. We recruited medical oncologists through direct emails to the Principal Investigator's personal contacts (n=29); direct messages via Doximity, an online networking service for medical professionals (n=17), using a "thoracic oncology" ; and direct messages via X (formerly Twitter) (n=4). Efforts were taken to sample equally from 4 US geographic regions (Northeast, South, Midwest, West).
Who may be able to join
- Medical oncologists who treat lung cancer
Who cannot join
- Medical oncologists who do not see lung cancer patients
Trial locations and contacts
1 site in 1 country.
United States1 site
Abramson Cancer Center of the University of Pennsylvania
Philadelphia, Pennsylvania, 19104
About this study
Nearly half of cancer patients in the US will receive care that is inconsistent with their wishes prior to death. Early advanced care planning (ACP) and palliative care improve goal-concordant care and symptoms and reduce unnecessary utilization. A promising strategy to increase ACP and palliative care is to identify patients at risk of mortality earlier in the disease course in order to target these services. Machine learning (ML) algorithms have been used in various industries, including medicine, to accurately predict risk of adverse outcomes and direct earlier resources. "Human-machine collaborations" - systems that leverage both ML and human intuition - have been shown to improve predictions and decision-making in various situations, but it is not known whether human-machine collaborations can improve prognostic accuracy and lead to greater and earlier ACP and palliative care. In this study, we contacted a national sample of medical oncologists and invited them complete a vignette-based survey. Our goal was to examine the association of exposure to ML mortality risk predictions with clinicians' prognostic accuracy and decision-making. We presented a series of six vignettes describing three clinical scenarios specific to a patient with advanced non-small cell lung cancer (aNSCLC) that differ by age, gender, performance status, smoking history, extent of disease, symptoms and molecular status. We will use these vignette-based surveys to examine the association of exposure to ML mortality risk predictions with medical oncologists' prognostic accuracy and decision-making.
- Conditions
- Oncology
- Being tested
- Survey
- Medical subject headings
- Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
SurveyOther
The study consisted of a 3 × 3 online factorial experiment employing a survey instrument hosted via Qualtrics presenting describing three patient vignettes. The three patient vignettes varied by various clinical characteristics including age, gender, performance status, smoking history, extent of disease, symptoms and molecular status. Each patient had advanced non-small cell lung cancer (aNSCLC). Each vignette had two parts: Part 1 described the case history for one of the three patients, after which prognostic estimates and medical decision-making was assessed (i.e. 1, 2, 3). Part 2 immediately followed and described the same vignette from the same patient with added information from a hypothetical ML predictive algorithm (i.e. A, B, C). The order of the vignettes in each survey was randomized with regard to presentation strategies for the ML risk predictions, so that there were 6 versions of the survey to which each participant was randomized.
| Study group | Role | What they receive |
|---|---|---|
| 1A 2B 3C | 1. Intermediate; 1.A. Reference dependent; 2. Poor; 2.B. Absolute prognosis; 3. Good; 3.C. Both Receives: Other: Survey | |
| 1A 2C 3B | 1. Intermediate; 1.A. Reference dependent; 2. Poor; 2.C. Both; 3. Good; 3.B. Absolute prognosis Receives: Other: Survey | |
| 1B 2A 3C | 1. Intermediate; 1.B. Absolute; 2. Poor; 2.A. Reference dependent; 3. Good; 3.C. Both Receives: Other: Survey | |
| 1B 2C 3A | 1. Intermediate; 1.B. Absolute; 2. Poor; 2.C. Both; 3. Good; 3.A. Reference dependent Receives: Other: Survey | |
| 1C 2A 3B | 1. Intermediate; 1.C. Both; 2. Poor; 2.A. Reference dependent; 3. Good; 3.B. Absolute Receives: Other: Survey | |
| 1C 2B 3A | 1. Intermediate; 1.C. Both; 2. Poor; 2.B. Absolute; 3. Good; 3.A. Reference dependent Receives: Other: Survey |
What is being measured
The primary outcome is the main question the study is designed to answer.
Prognostic accuracy as assessed via survey
Prognostic estimates were measured using two items administered after Parts 1 and 2 of each of the 3 vignettes:
- What is your anticipated life expectancy for this patient, in months?
- What do you think is the likelihood that she will die within 12 months? Please provide a percentage on a scale of 0% to 100%.
Accurate prognoses were defined as whether the reported life expectancy estimate was within 33% of the LCPI estimate, as modified after the focus groups. Participants answered the first question in months and the second question as a percentage between 0-100%.
Measured: Up to 3 months
Secondary outcomes (2)
Advance care planning decisions as assessed via survey
ACP decision-making was assessed using the following item administered after Parts 1 and 2 of each of the 3 vignettes:
- Would you have a discussion about advance care planning at this point in her disease course?
Each question was operationalized as a Yes/No answer and was followed by a free response box asking, "Please share your reason for this decision."
Measured: Up to 3 months
Palliative care referral as assessed via survey
Palliative care referral was assessed using the following item administered after Parts 1 and 2 of each of the 3 vignettes:
- Would you refer him/her to a palliative care specialist at this point in her disease course?
Each question was operationalized as a Yes/No answer and was followed by a free response box asking, "Please share your reason for this decision."
Measured: Up to 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Jun 2024
- Study start13 Mar 2023
- Primary completion31 Jul 2023
- Study completion31 Dec 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for OncologyStudies of the same condition, open and under way.
- More trials run by Abramson Cancer Center at Penn MedicineThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 52 participants (actual)
Sponsor and investigators
- Lead sponsor
- Abramson Cancer Center at Penn Medicine
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06463977
- Sponsor's protocol ID
- UPCC 10524
- Other IDs
- 850382
- Status last verified
- Nov 2024
- First submitted
- 30 Apr 2024
- Last updated
- 21 Nov 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 21 Nov 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Using Surveys to Examine the Association of Exposure to ML Mortality Risk Predictions With Medical Oncologists' Prognostic Accuracy and Decision-making
- ClinicalTrials.gov ID
- NCT06463977
- Acronym / sponsor ID
- UPCC 10524
- Conditions
- Oncology
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 52 participants (actual)
- Lead sponsor
- Abramson Cancer Center at Penn MedicineUniversity, hospital or other
- Study start
- 13 Mar 2023 (actual)
- Primary completion
- 31 Jul 2023 (actual)
- Study completion
- 31 Dec 2023 (actual)
- First posted
- 18 Jun 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Nearly half of cancer patients in the US will receive care that is inconsistent with their wishes prior to death. Early advanced care planning (ACP) and palliative care improve goal-concordant care and symptoms and reduce unnecessary utilization. A promising strategy to increase ACP and palliative care is to identify patients at risk of mortality earlier in the disease course in order to target these services. Machine learning (ML) algorithms have been used in various industries, including medicine, to accurately predict risk of adverse outcomes and direct earlier resources. "Human-machine collaborations" - systems that leverage both ML and human intuition - have been shown to improve predictions and decision-making in various situations, but it is not known whether human-machine collaborations can improve prognostic accuracy and lead to greater and earlier ACP and palliative care. In this study, we contacted a national sample of medical oncologists and invited them complete a vignette-based survey. Our goal was to examine the association of exposure to ML mortality risk predictions with clinicians' prognostic accuracy and decision-making. We presented a series of six vignettes describing three clinical scenarios specific to a patient with advanced non-small cell lung cancer (aNSCLC) that differ by age, gender, performance status, smoking history, extent of disease, symptoms and molecular status. We will use these vignette-based surveys to examine the association of exposure to ML mortality risk predictions with medical oncologists' prognostic accuracy and decision-making.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 6
| Arm | Type | Receives |
|---|---|---|
| 1A 2B 3C | – | Other: Survey1. Intermediate; 1.A. Reference dependent; 2. Poor; 2.B. Absolute prognosis; 3. Good; 3.C. Both |
| 1A 2C 3B | – | Other: Survey1. Intermediate; 1.A. Reference dependent; 2. Poor; 2.C. Both; 3. Good; 3.B. Absolute prognosis |
| 1B 2A 3C | – | Other: Survey1. Intermediate; 1.B. Absolute; 2. Poor; 2.A. Reference dependent; 3. Good; 3.C. Both |
| 1B 2C 3A | – | Other: Survey1. Intermediate; 1.B. Absolute; 2. Poor; 2.C. Both; 3. Good; 3.A. Reference dependent |
| 1C 2A 3B | – | Other: Survey1. Intermediate; 1.C. Both; 2. Poor; 2.A. Reference dependent; 3. Good; 3.B. Absolute |
| 1C 2B 3A | – | Other: Survey1. Intermediate; 1.C. Both; 2. Poor; 2.B. Absolute; 3. Good; 3.A. Reference dependent |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The study population consisted of a convenience sample of practicing medical oncologists who treated lung cancer in the US. We recruited medical oncologists through direct emails to the Principal Investigator's personal contacts (n=29); direct messages via Doximity, an online networking service for medical professionals (n=17), using a "thoracic oncology" ; and direct messages via X (formerly Twitter) (n=4). Efforts were taken to sample equally from 4 US geographic regions (Northeast, South, Midwest, West).
Key inclusion criteria (1 listed)
- Medical oncologists who treat lung cancer
Key exclusion criteria (1 listed)
- Medical oncologists who do not see lung cancer patients
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| SurveyThe study consisted of a 3 × 3 online factorial experiment employing a survey instrument hosted via Qualtrics presenting describing three patient vignettes. The three patient vignettes varied by various clinical characteristics including age, gender, performance status, smoking history, extent of disease, symptoms and molecular status. Each patient had advanced non-small cell lung cancer (aNSCLC). Each vignette had two parts: Part 1 described the case history for one of the three patients, after which prognostic estimates and medical decision-making was assessed (i.e. 1, 2, 3). Part 2 immediately followed and described the same vignette from the same patient with added information from a hypothetical ML predictive algorithm (i.e. A, B, C). The order of the vignettes in each survey was randomized with regard to presentation strategies for the ML risk predictions, so that there were 6 versions of the survey to which each participant was randomized. | Other | 1A 2B 3C, 1A 2C 3B, 1B 2A 3C, 1B 2C 3A, 1C 2A 3B, 1C 2B 3A |
Primary outcome
Prognostic accuracy as assessed via survey
Time frame: Up to 3 months
Prognostic estimates were measured using two items administered after Parts 1 and 2 of each of the 3 vignettes: 1. What is your anticipated life expectancy for this patient, in months? 2. What do you think is the likelihood that she will die within 12 months? Please provide a percentage on a scale of 0% to 100%. Accurate prognoses were defined as whether the reported life expectancy estimate was within 33% of the LCPI estimate, as modified after the focus groups. Participants answered the first question in months and the second question as a percentage between 0-100%.
2 secondary outcomes
Advance care planning decisions as assessed via survey
Time frame: Up to 3 months
Palliative care referral as assessed via survey
Time frame: Up to 3 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Nov 2024.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Oncology, run by Abramson Cancer Center at Penn Medicine at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 21 Nov 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Prognostic accuracy as assessed via survey (time frame: Up to 3 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 21 Nov 2024.