Publications

IMPORTANCE

Both noninvasive anatomic and functional testing strategies are now routinely used as initial workup in patients with low-risk stable chest pain (SCP).

OBJECTIVE

To determine whether anatomic approaches (ie, coronary computed tomography angiography [CTA] and coronary CTA supplemented with noninvasive fractional flow reserve [FFRCT], performed in patients with 30% to 69% stenosis) are cost-effective compared with functional testing for the assessment of low-risk SCP.

DESIGN, SETTING, AND PARTICIPANTS

This cost-effectiveness analysis used an individual-based Markov microsimulation model for low-risk SCP. The model was developed using patient data from the Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE) trial. The model was validated by comparing model outcomes with outcomes observed in the PROMISE trial for anatomic (coronary CTA) and functional (stress testing) strategies, including diagnostic test results, referral to invasive coronary angiography (ICA), coronary revascularization, incident major adverse cardiovascular event (MACE), and costs during 60 days and 2 years. The validated model was used to determine whether anatomic approaches are cost-effective over a lifetime compared with functional testing.

EXPOSURE

Choice of index test for evaluation of low-risk SCP.

MAIN OUTCOMES AND MEASURES

Downstream ICA and coronary revascularization, MACE (death, nonfatal myocardial infarction), cost, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio (ICER) of competing strategies.

RESULTS

The model cohort included 10 003 individual patients (median [interquartile range] age, 60.0 [54.4-65.9] years; 5270 [52.7%] women; 7693 [77.4%] White individuals), who entered the model 100 times. The Markov model accurately estimated the test assignment, results of anatomic and functional index testing, referral to ICA, revascularization, MACE, and costs at 60 days and 2 years compared with observed data in PROMISE (eg, coronary CTA: ICA, 12.2% [95% CI, 10.9%-13.5%] vs 12.3% [95% CI, 12.2%-12.4%]; revascularization, 6.2% [95% CI, 5.5%-6.9%] vs 6.4% [95% CI, 6.3%-6.5%]; functional strategy: ICA, 8.1% [95% CI, 7.4%-8.9%] vs 8.2% [95% CI, 8.1%-8.3%]; revascularization, 3.2% [95% CI, 2.7%-3.7%] vs 3.3% [95% CI, 3.2%-3.4%]; 2-year MACE rates: coronary CTA, 2.1% [95% CI, 1.7%-2.5%] vs 2.3% [95% CI, 2.2%-2.4%]; functional strategy, 2.2% [95% CI, 1.8%-2.6%] vs 2.4% [95% CI, 2.3%-2.4%]). Anatomic approaches led to higher ICA and revascularization rates at 60 days, 2 years, and 5 years compared with functional testing but were more effective in patient selection for ICA (eg, 60-day revascularization-to-ICA ratio, CTA: 53.7% [95% CI, 53.3%-54.0%]; CTA with FFRCT: 59.5% [95% CI, 59.2%-59.8%]; functional testing: 40.7% [95% CI, 40.4%-50.0%]). Over a lifetime, anatomic approaches gained an additional 6 months in perfect health compared with functional testing (CTA, 25.16 [95% CI, 25.14-25.19] QALYs; CTA with FFRCT, 25.14 [95% CI, 25.12-25.17] QALYs; functional testing, 24.68 [95% CI, 24.66-24.70] QALYs). Anatomic strategies were less costly and more effective; thus, CTA with FFRCT dominated and CTA alone was cost-effective (ICERs ranged from $1912/QALY for women and $3,559/QALY for men) compared with functional testing. In probabilistic sensitivity analyses, anatomic approaches were cost-effective in more than 65% of scenarios, assuming a willingness-to-pay threshold of $100 000/QALY.

CONCLUSIONS AND RELEVANCE

The results of this study suggest that anatomic strategies may present a more favorable initial diagnostic option in the evaluation of low-risk SCP compared with functional testing.

Immune effector cell (IEC) therapies offer durable and sustained remissions in significant numbers of patients with hematological cancers. While these unique immunotherapies have improved outcomes for pediatric and adult patients in a number of disease states, as 'living drugs,' their toxicity profiles, including cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS), differ markedly from conventional cancer therapeutics. At the time of article preparation, the US Food and Drug Administration (FDA) has approved tisagenlecleucel, axicabtagene ciloleucel, and brexucabtagene autoleucel, all of which are IEC therapies based on genetically modified T cells engineered to express chimeric antigen receptors (CARs), and additional products are expected to reach marketing authorization soon and to enter clinical development in due course. As IEC therapies, especially CAR T cell therapies, enter more widespread clinical use, there is a need for clear, cohesive recommendations on toxicity management, motivating the Society for Immunotherapy of Cancer (SITC) to convene an expert panel to develop a clinical practice guideline. The panel discussed the recognition and management of common toxicities in the context of IEC treatment, including baseline laboratory parameters for monitoring, timing to onset, and pharmacological interventions, ultimately forming evidence- and consensus-based recommendations to assist medical professionals in decision-making and to improve outcomes for patients.

The inverse mechano-electrical problem in cardiac electrophysiology is the attempt to reconstruct electrical excitation or action potential wave patterns from the heart's mechanical deformation that occurs in response to electrical excitation. Because heart muscle cells contract upon electrical excitation due to the excitation-contraction coupling mechanism, the resulting deformation of the heart should reflect macroscopic action potential wave phenomena. However, whether the relationship between macroscopic electrical and mechanical phenomena is well-defined and unique enough to be utilized for an inverse imaging technique in which mechanical activation mapping is used as a surrogate for electrical mapping has yet to be determined. Here, we provide a numerical proof-of-principle that deep learning can be used to solve the inverse mechano-electrical problem in phenomenological two- and three-dimensional computer simulations of the contracting heart wall, or in elastic excitable media, with muscle fiber anisotropy. We trained a convolutional autoencoder neural network to learn the complex relationship between electrical excitation, active stress, and tissue deformation during both focal or reentrant chaotic wave activity and, consequently, used the network to successfully estimate or reconstruct electrical excitation wave patterns from mechanical deformation in sheets and bulk-shaped tissues, even in the presence of noise and at low spatial resolutions. We demonstrate that even complicated three-dimensional electrical excitation wave phenomena, such as scroll waves and their vortex filaments, can be computed with very high reconstruction accuracies of about 95% from mechanical deformation using autoencoder neural networks, and we provide a comparison with results that were obtained previously with a physics- or knowledge-based approach.