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Outcomes of periprocedural continuation vs interruption of oral anticoagulation in transcatheter aortic valve replacement 认领 引用
1
作者 Aman Goyal Aqsa Shoaib +7 位作者 Areeba Fareed Sara Jawed Muhammad Taha Khan Najwa Salim Ushna Zameer Amna Siddiqui Tanya Thakur Samia Aziz Sulaiman 《World Journal of Cardiology》 2025年第3期73-82,共10页
BACKGROUND Up to one-third of patients undergoing transcatheter aortic valve replacement(TAVR)have an indication for oral anticoagulation(OAC),primarily due to underlying atrial fibrillation.The optimal approach conce... BACKGROUND Up to one-third of patients undergoing transcatheter aortic valve replacement(TAVR)have an indication for oral anticoagulation(OAC),primarily due to underlying atrial fibrillation.The optimal approach concerning periprocedural continuation vs interruption of OAC in patients undergoing TAVR remains uncertain,which our meta-analysis aims to address.AIM To explore safety and efficacy outcomes for patients undergoing TAVR,comparing periprocedural continuation vs interruption of OAC therapy.METHODS A literature search was conducted across major databases to retrieve eligible studies that assessed the safety and effectiveness of TAVR with periprocedural continuous vs interrupted OAC.Data were pooled using a random-effects model with risk ratio(RR)and their 95%confidence interval(CI)as effect measures.All statistical analyses were conducted using Review Manager with statistical significance set at P<0.05.RESULTS Four studies were included,encompassing a total of 1813 patients with a mean age of 80.6 years and 49.8%males.A total of 733 patients underwent OAC interruption and 1080 continued.Stroke incidence was significantly lower in the OAC continuation group(RR=0.62,95%CI:0.40-0.94;P=0.03).No significant differences in major vascular complications were found between the two groups(RR=0.95,95%CI:0.77-1.16;P=0.60)and major bleeding(RR=0.90,95%CI:0.72-1.12;P=0.33).All-cause mortality was non-significant between the two groups(RR=0.83,95%CI:0.57-1.20;P=0.32).CONCLUSION Continuation of OAC significantly reduced stroke risk,whereas it showed trends toward lower bleeding and mortality that were not statistically significant.Further large-scale studies are crucial to determine clinical significance. 展开更多
关键词 Transcatheter aortic valve replacement Oral anticoagulants Systematic review Cardiology Outcomes
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Comparison of ChatGPT and DeepSeek large language models in the diagnosis of pericarditis 认领 引用
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作者 Aman Goyal Samia Aziz Sulaiman +6 位作者 Abdallah Alaarag Waseem Hoshan Priya Goyal Viraj Shah Mohamed Daoud Gauranga Mahalwar Abu Baker Sheikh 《World Journal of Cardiology》 2025年第8期96-100,共5页
BACKGROUND The integration of sophisticated large language models(LLMs)into healthcare has recently garnered significant attention due to their ability to leverage deep learning techniques to process vast datasets and... BACKGROUND The integration of sophisticated large language models(LLMs)into healthcare has recently garnered significant attention due to their ability to leverage deep learning techniques to process vast datasets and generate contextually accurate,human-like responses.These models have been previously applied in medical diagnostics,such as in the evaluation of oral lesions.Given the high rate of missed diagnoses in pericarditis,LLMs may support clinicians in generating differential diagnoses-particularly in atypical cases where risk stratification and early identi-fication are critical to preventing serious complications such as constrictive pericarditis and pericardial tamponade.AIM To compare the accuracy of LLMs in assisting the diagnosis of pericarditis as risk stratification tools.METHODS A PubMed search was conducted using the keyword“pericarditis”,applying filters for“case reports”.Data from relevant cases were extracted.Inclusion criteria consisted of English-language reports involving patients aged 18 years or older with a confirmed diagnosis of acute pericarditis.The diagnostic capabilities of ChatGPT o1 and DeepThink-R1 were assessed by evaluating whether pericarditis was included in the top three differential diagnoses and as the sole provisional diagnosis.Each case was classified as either“yes”or“no”for inclusion.RESULTS From the initial search,220 studies were identified,of which 16 case reports met the inclusion criteria.In assessing risk stratification for acute pericarditis,ChatGPT o1 correctly identified the condition in 10 of 16 cases(62.5%)in the differential diagnosis and in 8 of 16 cases(50.0%)as the provisional diagnosis.DeepThink-R1 identified it in 8 of 16 cases(50.0%)and 6 of 16 cases(37.5%),respectively.ChatGPT o1 demonstrated higher accuracy than DeepThink-R1 in identifying pericarditis.CONCLUSION Further research with larger sample sizes and optimized prompt engineering is warranted to improve diagnostic accuracy,particularly in atypical presentations. 展开更多
关键词 Artificial intelligence Cardiology Pericarditis Diagnostics
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Accuracy of artificial intelligence in meta-analysis:A comparative study of ChatGPT 4.0 and traditional methods in data synthesis 认领 引用
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作者 Aman Goyal Muhammad Daoud Tariq +5 位作者 Areeba Ahsan Muhammad Hamza Khan Amna Zaheer Hritvik Jain Surabhi Maheshwari Andrei Brateanu 《World Journal of Methodology》 2025年第4期324-332,共9页
BACKGROUND Meta-analysis is a critical tool in evidence-based medicine,particularly in cardiology,where it synthesizes data from multiple studies to inform clinical decisions.This study explored the potential of using... BACKGROUND Meta-analysis is a critical tool in evidence-based medicine,particularly in cardiology,where it synthesizes data from multiple studies to inform clinical decisions.This study explored the potential of using ChatGPT to streamline and enhance the meta-analysis process.AIM To investigate the potential of ChatGPT to conduct meta-analyses in interventional cardiology by comparing the results of ChatGPT-generated analyses with those of randomly selected,human-conducted meta-analyses on the same topic.METHODS We systematically searched PubMed for meta-analyses on interventional cardiology published in 2024.Five metaanalyses were randomly chosen.ChatGPT 4.0 was used to perform meta-analyses on the extracted data.We compared the results from ChatGPT with the original meta-analyses,focusing on key effect sizes,such as risk ratios(RR),hazard ratios,and odds ratios,along with their confidence intervals(CI)and P values.RESULTS The ChatGPT results showed high concordance with those of the original meta-analyses.For most outcomes,the effect measures and P values generated by ChatGPT closely matched those of the original studies,except for the RR of stent thrombosis in the Sreenivasan et al study,where ChatGPT reported a non-significant effect size,while the original study found it to be statistically significant.While minor discrepancies were observed in specific CI and P values,these differences did not alter the overall conclusions drawn from the analyses.CONCLUSION Our findings suggest the potential of ChatGPT in conducting meta-analyses in interventional cardiology.However,further research is needed to address the limitations of transparency and potential data quality issues,ensuring that AI-generated analyses are robust and trustworthy for clinical decision-making. 展开更多
关键词 ChatGPT Artificial intelligence Large language model Meta-analysis Statistical analysis Methodology Cardiology
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Quantification of Ride Comfort Using Musculoskeletal Mathematical Model Considering Vehicle Behavior 认领 引用
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作者 Junya Tanehashi Szuchi Chang +4 位作者 Takahiro Hirosei Masaki Izawa Aman Goyal Ayumi Takahashi Kazuhito Misaji 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第6期2287-2306,共20页
This research aims to quantify driver ride comfort due to changes in damper characteristics between comfort mode and sport mode,considering the vehicle’s inertial behavior.The comfort of riding in an automobile has b... This research aims to quantify driver ride comfort due to changes in damper characteristics between comfort mode and sport mode,considering the vehicle’s inertial behavior.The comfort of riding in an automobile has been evaluated in recent years on the basis of a subjective sensory evaluation given by the driver.However,reflecting driving sensations in design work to improve ride comfort is abstract in nature and difficult to express theoretically.Therefore,we evaluated the human body’s effects while driving scientifically by quantifying the driver’s behavior while operating the steering wheel and the behavior of the automobile while in motion using physical quantities.To this end,we collected driver and vehicle data using amotion capture system and vehicle CAN and IMU sensors.We also constructed a three-dimensional musculoskeletal mathematical model to simulate driver movements and calculate the power and amount of energy per unit of time used for driving the joints and muscles of the human body.Here,we used comfort mode and sport mode to compare damper characteristics in terms of hardness.In comfort mode,damper characteristics are soft and steering stability is mild,but vibration from the road is not easily transmitted to the driver making for a lighter load on the driver.In sport mode,on the other hand,damper characteristics are hard and steering stability is comparatively better.Still,vibration from the road is easily transmitted to the driver,whichmakes it easy for a load to be placed on the driver.As a result of this comparison,it was found that a load was most likely to be applied to the driver’s neck.This result in relation to the neck joint can therefore be treated as an objective measure for quantifying ride comfort. 展开更多
关键词 Human engineering biomechanics driver’s sense of fatigue double lane change musculoskeletal mathematical model
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