ABSTRACT

BACKGROUND: Accurate diagnostic and prognostic predictions of venous thromboembolism (VTE) are crucial for VTE management. Artificial intelligence (AI) enables autonomous identification of the most predictive patterns from large complex data. Although evidence regarding its performance in VTE prediction is emerging, a comprehensive analysis of performance is lacking.

AIMS: To systematically review the performance of AI in the diagnosis and prediction of VTE and compare it to clinical risk assessment models (RAMs) or logistic regression models.

METHODS: A systematic literature search was performed using PubMed, MEDLINE, EMBASE, and Web of Science from inception to April 20, 2021. Search terms included "artificial intelligence" and "venous thromboembolism." Eligible criteria were original studies evaluating AI in the prediction of VTE in adults and reporting one of the following outcomes: sensitivity, specificity, positive predictive value, negative predictive value, or area under receiver operating curve (AUC). Risks of bias were assessed using the PROBAST tool. Unpaired t-test was performed to compare the mean AUC from AI versus conventional methods (RAMs or logistic regression models).

RESULTS: A total of 20 studies were included. Number of participants ranged from 31 to 111 888. The AI-based models included artificial neural network (six studies), support vector machines (four studies), Bayesian methods (one study), super learner ensemble (one study), genetic programming (one study), unspecified machine learning models (two studies), and multiple machine learning models (five studies). Twelve studies (60%) had both training and testing cohorts. Among 14 studies (70%) where AUCs were reported, the mean AUC for AI versus conventional methods were 0.79 (95% CI: 0.74-0.85) versus 0.61 (95% CI: 0.54-0.68), respectively (p < .001). However, the good to excellent discriminative performance of AI methods is unlikely to be replicated when used in clinical practice, because most studies had high risk of bias due to missing data handling and outcome determination.

CONCLUSION: The use of AI appears to improve the accuracy of diagnostic and prognostic prediction of VTE over conventional risk models; however, there was a high risk of bias observed across studies. Future studies should focus on transparent reporting, external validation, and clinical application of these models.

PMID:37794526 | DOI:10.1111/ejh.14110

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Cardiotoxicity News

PubMed articles on: Cardio-Oncology

Circulating Cardiovascular Biomarkers in Cancer Therapeutics-Related Cardiotoxicity: Review of Critical Challenges, Solutions, and Future Directions

J Am Heart Assoc. 2023 Oct 27:e029574. doi: 10.1161/JAHA.123.029574. Online ahead of print.

ABSTRACT

Cardiotoxicity is a growing concern in the oncology population. Transthoracic echocardiography and multigated acquisition scans have been used for surveillance but are relatively insensitive and resource intensive. Innovative imaging techniques are constrained by cost and availability. More sensitive, cost-effective cardiotoxicity surveillance strategies are needed. Circulating cardiovascular biomarkers could provide a sensitive, low-cost solution. Biomarkers such as troponins, natriuretic peptides (NPs), novel upstream signals of oxidative stress, inflammation, and fibrosis as well as panomic technologies have shown substantial promise, and guidelines recommend baseline measurement of troponins and NPs in all patients receiving potential cardiotoxins. Nonetheless, supporting evidence has been hampered by several limitations. Previous reviews have provided valuable perspectives on biomarkers in cancer populations, but important analytic aspects remain to be examined in depth. This review provides comprehensive assessment of critical challenges and solutions in this field, with focus on analytical issues relating to biomarker measurement and interpretation. Examination of evidence pertaining to common and serious forms of cardiotoxicity reveals that improved study designs incorporating larger, more diverse populations, registry-based approaches, and refinement of current definitions are key. Further efforts to harmonize biomarker methodologies including centralized biobanking and analyses, novel decision limits, and head-to-head comparisons are needed. Multimarker algorithms incorporating machine learning may allow rapid, personalized risk assessment. These improvements will not only augment the predictive value of circulating biomarkers in cardiotoxicity but may elucidate both direct and indirect relationships between cardiovascular disease and cancer, allowing biomarkers a greater role in the development and success of novel anticancer therapies.

PMID:37889193 | DOI:10.1161/JAHA.123.029574

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PubMed articles on: Cardio-Oncology

Recent advances in pluripotent stem cell-derived cardiac organoids and heart-on-chip applications for studying anti-cancer drug-induced cardiotoxicity

Cell Biol Toxicol. 2023 Oct 27. doi: 10.1007/s10565-023-09835-4. Online ahead of print.