RESUMEN
BACKGROUND: Stereotactic arrhythmia radioablation (STAR) is a therapeutic option for ventricular tachycardia (VT) where catheter-based ablation is not feasible or has previously failed. Target definition and its transfer from electro-anatomic maps (EAM) to radiotherapy treatment planning systems (TPS) is challenging and operator-dependent. Software solutions have been developed to register EAM with cardiac CT and semi-automatically transfer 2D target surface data into 3D CT volume coordinates. Results of a cross-validation study of two conceptually different software solutions using data from the RAVENTA trial (NCT03867747) are reported. METHODS: Clinical Target Volumes (CTVs) were created from target regions delineated on EAM using two conceptually different approaches by separate investigators on data of 10 patients, blinded to each other's results. Targets were transferred using 3D-3D registration and 2D-3D registration, respectively. The resulting CTVs were compared in a core-lab using two complementary analysis software packages for structure similarity and geometric characteristics. RESULTS: Volumes and surface areas of the CTVs created by both methods were comparable: 14.88 ± 11.72 ml versus 15.15 ± 11.35 ml and 44.29 ± 33.63 cm2 versus 46.43 ± 35.13 cm2. The Dice-coefficient was 0.84 ± 0.04; median surface-distance and Hausdorff-distance were 0.53 ± 0.37 mm and 6.91 ± 2.26 mm, respectively. The 3D-center-of-mass difference was 3.62 ± 0.99 mm. Geometrical volume similarity was 0.94 ± 0.05 %. CONCLUSION: The STAR targets transferred from EAM to TPS using both software solutions resulted in nearly identical 3D structures. Both solutions can be used for QA (quality assurance) and EAM-to-TPS transfer of STAR-targets. Semi-automated methods could potentially help to avoid mistargeting in STAR and offer standardized workflows for methodically harmonized treatments.
RESUMEN
Aims: Female physicians are underrepresented in invasive electrophysiology (EP) for multiple reasons. Despite an increasing focus on the topic, it is unclear what aspects are predominant. Methods and results: We conducted a survey on career paths of current or former EP fellows in Germany to elucidate how gender and family affected their careers. 231 fellows (24.2% female) were invited. 110 participants completed the survey (30.9% female, mean age 41.0 ± 5.0 years, and 79.1% with children). Female and male participants with children reported similar career goals and achievements before parenthood, but afterwards women changed their career paths more often. Major reasons were personal priorities followed by lack of flexibility at work and at home. Women covered the majority of childcare. At the time of the survey, 80.0% of women and 96.4% of men with a former career goal of invasive EP were active in invasive EP. Independent of age, women were in lower-level positions, had accomplished fewer professional achievements, were less satisfied with their work and had fewer children. 56.5% of women did not feel supported by their employers regarding family issues. 82.6% reported there was no satisfactory day care. 69.6% were unable to continue to follow their career during pregnancy, mostly due to restrictions by employers (75.0%). Dedicated policies for pregnant workers or support programmes were scarce. Conclusion: Beside the distribution of childcare at home, lack of flexibility and support by employers as well as working and fluoroscopy restrictions during pregnancy hamper women in EP and should be addressed.
RESUMEN
AIMS: Natural language processing chatbots (NLPC) can be used to gather information for medical content. However, these tools contain a potential risk of misinformation. This study aims to evaluate different aspects of responses given by different NLPCs on questions about atrial fibrillation (AF) and clinical implantable electronic devices (CIED). METHODS AND RESULTS: Questions were entered into three different NLPC interfaces. Responses were evaluated with regard to appropriateness, comprehensibility, appearance of confabulation, absence of relevant content, and recommendations given for clinically relevant decisions. Moreover, readability was assessed by calculating word count and Flesch Reading Ease score. 52, 60, and 84% of responses on AF and 16, 72, and 88% on CIEDs were evaluated to be appropriate for all responses given by Google Bard, (GB) Bing Chat (BC) and ChatGPT Plus (CGP), respectively. Assessment of comprehensibility showed that 96, 88, and 92% of responses on AF and 92 and 88%, and 100% on CIEDs were comprehensible for all responses created by GB, BC, and CGP, respectively. Readability varied between different NLPCs. Relevant aspects were missing in 52% (GB), 60% (BC), and 24% (CGP) for AF, and in 92% (GB), 88% (BC), and 52% (CGP) for CIEDs. CONCLUSION: Responses generated by an NLPC are mostly easy to understand with varying readability between the different NLPCs. The appropriateness of responses is limited and varies between different NLPCs. Important aspects are often missed to be mentioned. Thus, chatbots should be used with caution to gather medical information about cardiac arrhythmias and devices.