ArticleCardio-oncology (London, England)2023
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.
Article in Cardio-oncology (London, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05377320 (PAtient Similarity for Decision-Making in Prevention of Cardiovascular Toxicity), which is not on this map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
PAtient Similarity for Decision-Making in Prevention of Cardiovascular Toxicity (PACT): A Feasibility Study
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Data-driven decision making in patient management: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Trial
- Artificial Intelligence for Prognostic Modelling and Adaptive Treatment Monitoring in Radiation Oncology.Cureus · 2026Review
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- A scoping review of web-based, interactive, personalized decision-making tools available to support breast cancer treatment and survivorship care.Journal of cancer survivorship : research and practice · 2025Article
- Applications, challenges and future directions of artificial intelligence in cardio-oncology.European journal of clinical investigation · 2025Review
- Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review.Current cardiology reports · 2025Review
- Leveraging artificial intelligence for collaborative care planning: Innovations and impacts in shared decision-making - A systematic review.Open medicine (Warsaw, Poland) · 2025Review
- Artificial intelligence: Applications in cardio-oncology and potential impact on racial disparities.American heart journal plus : cardiology research and practice · 2024Article
- Using machine learning in pediatric cardio-oncology: we have the questions, we need the answers.Cardio-oncology (London, England) · 2024Article
- Unsupervised machine learning identifies distinct phenotypes in cardiac complications of pediatric patients treated with anthracyclines.Cardio-oncology (London, England) · 2024Article
- Identifications of Similarity Metrics for Patients With Cancer: Protocol for a Scoping Review.JMIR research protocols · 2024Article
- Multimedia Mixed Reality Interactive Shared Decision-Making Game in Children with Moderate to Severe Atopic Dermatitis, a Pilot Study.Children (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 6 institutions in 1 country.
Funding
Abstract
backgroundThe 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.
objectivesTo 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.
designThis 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. 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 registrationClinicalTrials.Gov Identifier: NCT05377320.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.