Establishing an interdisciplinary research team for cardio-oncology artificial intelligence informatics precision and health equity

Brown, Sherry-Ann; Sparapani, Rodney; Osinski, Kristen; Zhang, Jun; Blessing, Jeffrey; Cheng, Feixiong; Hamid, Abdulaziz; Berman, Generika; Lee, Kyla; BagheriMohamadiPour, Mehri; Lal, Jessica C.; Kothari, Anai N.; Caraballo, Pedro J.; Noseworthy, Peter A.; Hansen, Kathryn; Sun, Louise Y.; Crotty, Bradley H.; Cheng, Yee Chung; Olson, Jessica

doi:10.1016/j.ahjo.2022.100094

Cited by 15 publications

(9 citation statements)

References 26 publications

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“…Team science has been transforming academic research for decades and has helped shift production of knowledge away from a model of individual genius, to a combined effort between and within institutions [ 48 ].The leading C.H.I.R.P. physician has experience forming diverse research teams and networks that fit within this team science model [ 49 , 50 ]. Obstacles can be common in building complex, interdisciplinary team science research programs such as C.H.I.R.P.…”

Section: Discussionmentioning

confidence: 99%

Connected Health Innovation Research Program (C.H.I.R.P.): A bridge for digital health and wellness in cardiology and oncology

Maddula

MacLeod

Painter

et al. 2022

American Heart Journal Plus: Cardiology Research and Practice

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Section: Discussionmentioning

confidence: 99%

Connected Health Innovation Research Program (C.H.I.R.P.): A bridge for digital health and wellness in cardiology and oncology

Maddula

MacLeod

Painter

et al. 2022

American Heart Journal Plus: Cardiology Research and Practice

Self Cite

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“…Unplanned readmissions were identified by excluding elective readmissions using the elective variable in the database. International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM) codes were used to define CVD, including atrial fibrillation (I48), coronary artery disease (I20–I25, I252), cardiomegaly (I517), cardiomyopathy (I43, I427, I429, I420, I425), heart failure (I50), peripheral artery disease (I70, I74, I739), and stroke (I60–I63, I65–I66, I69, I672, I679, I6781–I6782) 15 .…”

Section: Methodsmentioning

confidence: 99%

Predicting unplanned readmission due to cardiovascular disease in hospitalized patients with cancer: a machine learning approach

Han,

Sohn,

et al. 2023

Sci Rep

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Cardiovascular disease (CVD) in cancer patients can affect the risk of unplanned readmissions, which have been reported to be costly and associated with worse mortality and prognosis. We aimed to demonstrate the feasibility of using machine learning techniques in predicting the risk of unplanned 180-day readmission attributable to CVD among hospitalized cancer patients using the 2017–2018 Nationwide Readmissions Database. We included hospitalized cancer patients, and the outcome was unplanned hospital readmission due to any CVD within 180 days after discharge. CVD included atrial fibrillation, coronary artery disease, heart failure, stroke, peripheral artery disease, cardiomegaly, and cardiomyopathy. Decision tree (DT), random forest, extreme gradient boost (XGBoost), and AdaBoost were implemented. Accuracy, precision, recall, F2 score, and receiver operating characteristic curve (AUC) were used to assess the model’s performance. Among 358,629 hospitalized patients with cancer, 5.86% (n = 21,021) experienced unplanned readmission due to any CVD. The three ensemble algorithms outperformed the DT, with the XGBoost displaying the best performance. We found length of stay, age, and cancer surgery were important predictors of CVD-related unplanned hospitalization in cancer patients. Machine learning models can predict the risk of unplanned readmission due to CVD among hospitalized cancer patients.

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“…The overall study cohort dataset of > 4,000 individuals includes demographic, physiological, laboratory, medication, medical history, and outcomes data relevant to cardiovascular risk stratification in individuals with a history of cancer, as well as cardiovascular imaging reports [30]. This cohort will be queried to identify ≥ 200 adult cancer survivors (including ethnic/racial minorities) clinically considered to be at intermediate, high, or very high cardiovascular risk following cancer therapy determined imprecisely based on demographic and comorbidity information [3,11,[31][32][33][34].…”

Section: Study Populationmentioning

confidence: 99%

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

Brown

Chung²,

Doshi³

et al. 2023

Cardio-Oncology

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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

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Establishing an interdisciplinary research team for cardio-oncology artificial intelligence informatics precision and health equity

Cited by 15 publications

References 26 publications

Connected Health Innovation Research Program (C.H.I.R.P.): A bridge for digital health and wellness in cardiology and oncology

Connected Health Innovation Research Program (C.H.I.R.P.): A bridge for digital health and wellness in cardiology and oncology

Predicting unplanned readmission due to cardiovascular disease in hospitalized patients with cancer: a machine learning approach

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

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