Patient similarity and other artificial intelligence machine learning algorithms in clinical decision aid for shared decision-making in the Prevention of Cardiovascular Toxicity (PACT): a feasibility trial design
Abstract Background The many improvements in cancer therapies have led to an increased number of survivors, which comes with a greater risk of consequent/subsequent cardiovascular disease. Identifying effective management strategies that can mitigate this risk of cardiovascular complications is vita...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-01-01
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| Series: | Cardio-Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40959-022-00151-0 |
| _version_ | 1811175708919070720 |
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| author | Sherry-Ann Brown Brian Y. Chung Krishna Doshi Abdulaziz Hamid Erin Pederson Ragasnehith Maddula Allen Hanna Indrajit Choudhuri Rodney Sparapani Mehri Bagheri Mohamadi Pour Jun Zhang Anai N. Kothari Patrick Collier Pedro Caraballo Peter Noseworthy Adelaide Arruda-Olson for the Cardio-Oncology Artificial Intelligence Informatics and Precision Equity (CAIPE) Research Team Investigators |
| author_facet | Sherry-Ann Brown Brian Y. Chung Krishna Doshi Abdulaziz Hamid Erin Pederson Ragasnehith Maddula Allen Hanna Indrajit Choudhuri Rodney Sparapani Mehri Bagheri Mohamadi Pour Jun Zhang Anai N. Kothari Patrick Collier Pedro Caraballo Peter Noseworthy Adelaide Arruda-Olson for the Cardio-Oncology Artificial Intelligence Informatics and Precision Equity (CAIPE) Research Team Investigators |
| author_sort | Sherry-Ann Brown |
| collection | DOAJ |
| description | Abstract Background The many improvements in cancer therapies have led to an increased number of survivors, which comes with a greater risk of consequent/subsequent cardiovascular disease. Identifying effective management strategies that can mitigate this risk of cardiovascular complications is vital. Therefore, developing computer-driven and personalized clinical decision aid interventions that can provide early detection of patients at risk, stratify that risk, and recommend specific cardio-oncology management guidelines and expert consensus recommendations is critically important. Objectives To assess the feasibility, acceptability, and utility of the use of an artificial intelligence (AI)-powered clinical decision aid tool in shared decision making between the cancer survivor patient and the cardiologist regarding prevention of cardiovascular disease. Design This is a single-center, double-arm, open-label, randomized interventional feasibility study. Our cardio-oncology cohort of > 4000 individuals from our Clinical Research Data Warehouse will be queried to identify at least 200 adult cancer survivors who meet the eligibility criteria. Study participants will be randomized into either the Clinical Decision Aid Group (where patients will use the clinical decision aid in addition to current practice) or the Control Group (current practice). The primary endpoint of this study is to assess for each patient encounter whether cardiovascular medications and imaging pursued were consistent with current medical society recommendations. Additionally, the perceptions of using the clinical decision tool will be evaluated based on patient and physician feedback through surveys and focus groups. Summary This trial will determine whether a clinical decision aid tool improves cancer survivors’ medication use and imaging surveillance recommendations aligned with current medical guidelines. Trial registration ClinicalTrials.Gov Identifier: NCT05377320 |
| first_indexed | 2024-04-10T19:40:09Z |
| format | Article |
| id | doaj.art-b26d5d15ae0c4ce999952205f414e93b |
| institution | Directory Open Access Journal |
| issn | 2057-3804 |
| language | English |
| last_indexed | 2024-04-10T19:40:09Z |
| publishDate | 2023-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Cardio-Oncology |
| spelling | doaj.art-b26d5d15ae0c4ce999952205f414e93b2023-01-29T12:22:53ZengBMCCardio-Oncology2057-38042023-01-019111010.1186/s40959-022-00151-0Patient similarity and other artificial intelligence machine learning algorithms in clinical decision aid for shared decision-making in the Prevention of Cardiovascular Toxicity (PACT): a feasibility trial designSherry-Ann Brown0Brian Y. Chung1Krishna Doshi2Abdulaziz Hamid3Erin Pederson4Ragasnehith Maddula5Allen Hanna6Indrajit Choudhuri7Rodney Sparapani8Mehri Bagheri Mohamadi Pour9Jun Zhang10Anai N. Kothari11Patrick Collier12Pedro Caraballo13Peter Noseworthy14Adelaide Arruda-Olson15for the Cardio-Oncology Artificial Intelligence Informatics and Precision Equity (CAIPE) Research Team InvestigatorsCardio-Oncology Program, Division of Cardiovascular Medicine, Medical College of WisconsinCancer Center, Medical College of WisconsinDepartment of Internal Medicine, Advocate Lutheran General HospitalMedical College of WisconsinMedical College of WisconsinMedical College of WisconsinUniversity of Wisconsin-MilwaukeeDepartment of Electrophysiology, Froedtert SouthInstitute for Health and Equity, Medical College of WisconsinDepartment of Computer Science, University of Wisconsin-MilwaukeeDepartment of Computer Science, University of Wisconsin-MilwaukeeDivision of Surgical Oncology, Department of Surgery, Medical College of WisconsinDepartment of Cardiovascular Medicine, Cleveland ClinicDepartment of Medicine, Mayo ClinicDepartment of Cardiovascular Medicine, Mayo ClinicDepartment of Cardiovascular Medicine, Mayo ClinicAbstract Background The many improvements in cancer therapies have led to an increased number of survivors, which comes with a greater risk of consequent/subsequent cardiovascular disease. Identifying effective management strategies that can mitigate this risk of cardiovascular complications is vital. Therefore, developing computer-driven and personalized clinical decision aid interventions that can provide early detection of patients at risk, stratify that risk, and recommend specific cardio-oncology management guidelines and expert consensus recommendations is critically important. Objectives To assess the feasibility, acceptability, and utility of the use of an artificial intelligence (AI)-powered clinical decision aid tool in shared decision making between the cancer survivor patient and the cardiologist regarding prevention of cardiovascular disease. Design This is a single-center, double-arm, open-label, randomized interventional feasibility study. Our cardio-oncology cohort of > 4000 individuals from our Clinical Research Data Warehouse will be queried to identify at least 200 adult cancer survivors who meet the eligibility criteria. Study participants will be randomized into either the Clinical Decision Aid Group (where patients will use the clinical decision aid in addition to current practice) or the Control Group (current practice). The primary endpoint of this study is to assess for each patient encounter whether cardiovascular medications and imaging pursued were consistent with current medical society recommendations. Additionally, the perceptions of using the clinical decision tool will be evaluated based on patient and physician feedback through surveys and focus groups. Summary This trial will determine whether a clinical decision aid tool improves cancer survivors’ medication use and imaging surveillance recommendations aligned with current medical guidelines. Trial registration ClinicalTrials.Gov Identifier: NCT05377320https://doi.org/10.1186/s40959-022-00151-0Cardio-oncologyCardiotoxicityCancer survivorsMachine learningArtificial intelligenceClinical decision aid |
| spellingShingle | Sherry-Ann Brown Brian Y. Chung Krishna Doshi Abdulaziz Hamid Erin Pederson Ragasnehith Maddula Allen Hanna Indrajit Choudhuri Rodney Sparapani Mehri Bagheri Mohamadi Pour Jun Zhang Anai N. Kothari Patrick Collier Pedro Caraballo Peter Noseworthy Adelaide Arruda-Olson for the Cardio-Oncology Artificial Intelligence Informatics and Precision Equity (CAIPE) Research Team Investigators Patient similarity and other artificial intelligence machine learning algorithms in clinical decision aid for shared decision-making in the Prevention of Cardiovascular Toxicity (PACT): a feasibility trial design Cardio-Oncology Cardio-oncology Cardiotoxicity Cancer survivors Machine learning Artificial intelligence Clinical decision aid |
| title | Patient similarity and other artificial intelligence machine learning algorithms in clinical decision aid for shared decision-making in the Prevention of Cardiovascular Toxicity (PACT): a feasibility trial design |
| title_full | Patient similarity and other artificial intelligence machine learning algorithms in clinical decision aid for shared decision-making in the Prevention of Cardiovascular Toxicity (PACT): a feasibility trial design |
| title_fullStr | Patient similarity and other artificial intelligence machine learning algorithms in clinical decision aid for shared decision-making in the Prevention of Cardiovascular Toxicity (PACT): a feasibility trial design |
| title_full_unstemmed | Patient similarity and other artificial intelligence machine learning algorithms in clinical decision aid for shared decision-making in the Prevention of Cardiovascular Toxicity (PACT): a feasibility trial design |
| title_short | Patient similarity and other artificial intelligence machine learning algorithms in clinical decision aid for shared decision-making in the Prevention of Cardiovascular Toxicity (PACT): a feasibility trial design |
| title_sort | patient similarity and other artificial intelligence machine learning algorithms in clinical decision aid for shared decision making in the prevention of cardiovascular toxicity pact a feasibility trial design |
| topic | Cardio-oncology Cardiotoxicity Cancer survivors Machine learning Artificial intelligence Clinical decision aid |
| url | https://doi.org/10.1186/s40959-022-00151-0 |
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