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Ávila P, Berruezo A, Jiménez-Candil J, Tercedor L, Calvo D, Arribas F, Fernández-Portales J, Merino JL, Hernández-Madrid A, Fernández-Avilés F, Arenal Á. Bayesian analysis of the Substrate Ablation vs. Antiarrhythmic Drug Therapy for Symptomatic Ventricular Tachycardia trial. Europace 2023; 25:euad181. [PMID: 37366571 PMCID: PMC10326301 DOI: 10.1093/europace/euad181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 06/21/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND AND AIMS Bayesian analyses can provide additional insights into the results of clinical trials, aiding in the decision-making process. We analysed the Substrate Ablation vs. Antiarrhythmic Drug Therapy for Symptomatic Ventricular Tachycardia (SURVIVE-VT) trial using Bayesian survival models. METHODS AND RESULTS The SURVIVE-VT trial randomized patients with ischaemic cardiomyopathy and monomorphic ventricular tachycardia (VT) to catheter ablation or antiarrhythmic drugs (AAD) as a first-line strategy. The primary outcome was a composite of cardiovascular death, appropriate implantable cardioverter-defibrillator shocks, unplanned heart failure hospitalizations, or severe treatment-related complications. We used informative, skeptical, and non-informative priors with different probabilities of large effects to compute the posterior distributions using Markov Chain Monte Carlo methods. We calculated the probabilities of hazard ratios (HR) being <1, <0.9, and <0.75, as well as 2-year survival estimates. Of the 144 randomized patients, 71 underwent catheter ablation and 73 received AAD. Regardless of the prior, catheter ablation had a >98% probability of reducing the primary outcome (HR < 1) and a >96% probability of achieving a reduction of >10% (HR < 0.9). The probability of a >25% (HR < 0.75) reduction of treatment-related complications was >90%. Catheter ablation had a high probability (>93%) of reducing incessant/slow undetected VT/electric storm, unplanned hospitalizations for ventricular arrhythmias, and overall cardiovascular admissions > 25%, with absolute differences of 15.2%, 21.2%, and 20.2%, respectively. CONCLUSION In patients with ischaemic cardiomyopathy and VT, catheter ablation as a first-line therapy resulted in a high probability of reducing several clinical outcomes compared to AAD. Our study highlights the value of Bayesian analysis in clinical trials and its potential for guiding treatment decisions. TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT03734562.
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Affiliation(s)
- Pablo Ávila
- Cardiology Department, Hospital General Universitario Gregorio Marañón, IiSGM, Universidad Complutense, CIBERCV, Dr Esquerdo 46, 28007, Madrid, Spain
| | - Antonio Berruezo
- Arrhythmia Unit, Cardiology Department, Hospital Clinic and Teknon Medical Centre, c/Villarroel 170, 08036, Barcelona, Spain
| | - Javier Jiménez-Candil
- Arrhythmia Unit, Cardiology Department, IBSAL-Hospital Universitario, Universidad de Salamanca, CIBERCV, Paseo San Vicente 58-182, 37007, Salamanca, Spain
| | - Luis Tercedor
- Arrhythmia Unit, Cardiology Department, Hospital Universitario Virgen de las Nieves, Avd. Fuerzas Armadas 2, 18014, Granada, Spain
| | - David Calvo
- Arrhythmia Unit, Cardiology Department, Hospital Universitario Central de Asturias, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Avd Roma, s/n, 33011, Oviedo, Spain
- Arrhythmia Unit, Cardiology Department, Hospital Clínico San Carlos, Prof Martín Lagos, S/N, Madrid, 28040, Spain
| | - Fernando Arribas
- Cardiology Department, Hospital Doce de Octubre, Av. de Córdoba, s/n, 28041, Madrid, Spain
| | - Javier Fernández-Portales
- Cardiology Department, Complejo Hospitalario Universitario de Cáceres, Av. de la Universidad 75, 10004, Cáceres, Spain
| | - José Luis Merino
- Arrhythmia Unit, Cardiology Department, Hospital Universitario La Paz, IdiPAZ, Universidad Autónoma, P.º de la Castellana 261, 28046, Madrid, Spain
| | - Antonio Hernández-Madrid
- Arrhythmia Unit, Hospital Ramón y Cajal, Universidad de Alcalá de Henares, M-607, 9, 100, 28034, Madrid, Spain
| | - Francisco Fernández-Avilés
- Cardiology Department, Hospital General Universitario Gregorio Marañón, IiSGM, Universidad Complutense, CIBERCV, Dr Esquerdo 46, 28007, Madrid, Spain
| | - Ángel Arenal
- Cardiology Department, Hospital General Universitario Gregorio Marañón, IiSGM, Universidad Complutense, CIBERCV, Dr Esquerdo 46, 28007, Madrid, Spain
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Msaouel P, Lee J, Karam JA, Thall PF. A Causal Framework for Making Individualized Treatment Decisions in Oncology. Cancers (Basel) 2022; 14:cancers14163923. [PMID: 36010916 PMCID: PMC9406391 DOI: 10.3390/cancers14163923] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/12/2022] [Accepted: 08/12/2022] [Indexed: 12/23/2022] Open
Abstract
Simple Summary Physicians routinely make individualized treatment decisions by accounting for the joint effects of patient prognostic covariates and treatments on clinical outcomes. Ideally, this is performed using historical randomized clinical trial (RCT) data. Randomization ensures that unbiased estimates of causal treatment effect parameters can be obtained from the historical RCT data and used to predict each new patient’s outcome based on the joint effect of their baseline covariates and each treatment being considered. However, this process becomes problematic if a patient seen in the clinic is very different from the patients who were enrolled in the RCT. That is, if a new patient does not satisfy the entry criteria of the RCT, then the patient does not belong to the population represented by the patients who were studied in the RCT. In such settings, it still may be possible to utilize the RCT data to help choose a new patient’s treatment. This may be achieved by combining the RCT data with data from other clinical trials, or possibly preclinical experiments, and using the combined dataset to predict the patient’s expected outcome for each treatment being considered. In such settings, combining data from multiple sources in a way that is statistically reliable is not entirely straightforward, and correctly identifying and estimating the effects of treatments and patient covariates on clinical outcomes can be complex. Causal diagrams provide a rational basis to guide this process. The first step is to construct a causal diagram that reflects the plausible relationships between treatment variables, patient covariates, and clinical outcomes. If the diagram is correct, it can be used to determine what additional data may be needed, how to combine data from multiple sources, how to formulate a statistical model for clinical outcomes as a function of treatment and covariates, and how to compute an unbiased treatment effect estimate for each new patient. We use adjuvant therapy of renal cell carcinoma to illustrate how causal diagrams may be used to guide these steps. Abstract We discuss how causal diagrams can be used by clinicians to make better individualized treatment decisions. Causal diagrams can distinguish between settings where clinical decisions can rely on a conventional additive regression model fit to data from a historical randomized clinical trial (RCT) to estimate treatment effects and settings where a different approach is needed. This may be because a new patient does not meet the RCT’s entry criteria, or a treatment’s effect is modified by biomarkers or other variables that act as mediators between treatment and outcome. In some settings, the problem can be addressed simply by including treatment–covariate interaction terms in the statistical regression model used to analyze the RCT dataset. However, if the RCT entry criteria exclude a new patient seen in the clinic, it may be necessary to combine the RCT data with external data from other RCTs, single-arm trials, or preclinical experiments evaluating biological treatment effects. For example, external data may show that treatment effects differ between histological subgroups not recorded in an RCT. A causal diagram may be used to decide whether external observational or experimental data should be obtained and combined with RCT data to compute statistical estimates for making individualized treatment decisions. We use adjuvant treatment of renal cell carcinoma as our motivating example to illustrate how to construct causal diagrams and apply them to guide clinical decisions.
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Affiliation(s)
- Pavlos Msaouel
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- David H. Koch Center for Applied Research of Genitourinary Cancers, The University of Texas, MD Anderson Cancer Center, Houston, TX 77030, USA
- Correspondence:
| | - Juhee Lee
- Department of Statistics, University of California, Santa Cruz, CA 95064, USA
| | - Jose A. Karam
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Urology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Peter F. Thall
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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