Cohort Profile: LIFE course study in CARdiovascular disease Epidemiology (LIFECARE)

Shafiq, Mahham; Fong, Alan Yean Yip; Tai, E. Shyong; Nang, Ei Ei Khaing; Wee, Hwee Lin; Adam, John M F; Woodward, Mark; Sritara, Piyamitr; Poulton, Richie; Sy, Rody G.; Venkataraman, Kavita

doi:10.1093/ije/dyy168

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…Judging from the 29% proportional mortality due to CVD in Thailand, up to 37% in Indonesia in 2014. and is included in the country with the most population in Southeast Asia 22 . The essential sociodemographic characteristics used are age, gender, race, education, employment status, marital status, smoking, and alcohol intake 23 .…”

Section: Discussionmentioning

confidence: 99%

Prevalence of Metabolic Disease among Adults in Indonesia: A Cross-Sectional Population-Based Survey

Fatimah,

Ismail

2024

JHEPR

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Introduction: Metabolic diseases were the leading cause of morbidity and mortality in the world. the World Health Organization, the effective delivery of public health messages has become increasingly complicated due to CKD as a complication of cardiovascular disease and diabetes. Studies have prevalence of metabolic disease among adults in Indonesia. These study aims were to determine the prevalence of kidney, cardiovascular, diabetes. Method: This study uses a cross-sectional study design used from Indonesia Family Life Survey Wave 5 (IFLS-5) organized by Rand Corporation conducted in 2014-2015. A statistical analysis is a descriptive observational study. The total number of respondents un this study was 27,718. Result: Respondents with adult criteria in Indonesia were 52.67% women aged 38.20(±13.64), 78.48% married, 32.94%elementary, and 59.89% urban residents. The prevalence of chronic kidney disease among adults in Indonesia was 1.43% (95% CI: 00.01 – 0.02). The prevalence of cardiovascular disease among adults in Indonesia was 1.64% (95% CI: 00.01 – 0.02). The prevalence of diabetes among adults in Indonesia was 2.33% (95% CI: 00.02 – 0.03). Conclusion: The prevalence of chronic kidney disease, cardiovascular disease, and diabetes in Indonesia is strongly by age and show a relationship between sufferers of these three diseases.

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Section: Discussionmentioning

confidence: 99%

Prevalence of Metabolic Disease among Adults in Indonesia: A Cross-Sectional Population-Based Survey

Fatimah,

Ismail

2024

JHEPR

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“…Despite the gradually decreasing age‐standardized CVD morbidity rates, CVDs continue to be the highest contributors to global mortality. [ 1 ] A growing body of evidence has demonstrated that minimally invasive surgery can be adopted as an intervention for CVDs.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the gradually decreasing age-standardized CVD morbidity rates, CVDs continue to be the highest contributors to global mortality. [1] A growing body of evidence has demonstrated that minimally invasive surgery can be adopted as an intervention for CVDs.Minimally invasive surgery has become a standard practice in the interventional medical field, often involving the use of multiple surgical instruments guided by medical images. Surgeons rely on the real-time processing and analysis of 2D or 3D medical images to integrate the patient's actual anatomy with the images.…”

mentioning

confidence: 99%

Guidewire Endpoint Detection Based on Pixel‐Adjacent Relation during Robot‐Assisted Intravascular Catheterization: In Vivo Mammalian Models

Du,

Yi,

Omisore

et al. 2024

Advanced Intelligent Systems

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Existing surgical guidewire endpoint localization methods in X‐ray images face challenges owing to their small size, simple appearance, nonrigid nature of objects, low signal‐to‐noise ratio of X‐ray images, and imbalance between the number of guidewire and background pixels, which lead to errors in surgical navigation. An eight‐neighborhood‐based method for increasing the localization accuracy of guidewire endpoint to improve the safety of interventional procedures is proposed herein. The proposed method includes two stages: 1) An improved U‐Net network is employed for segmenting the data of the guidewire to extract regions of interest containing guidewire endpoints with higher precision and to reduce interference from other anatomical structures and imaging artifacts. 2) The proposed method detects guidewire endpoints using the adjacent relationship between pixels in the eight‐neighborhood regions. This stage covers skeletonization extraction, removal of bifurcation points, and repair of fracture points. This study achieves mean pixel errors of 2.02 and 2.13 pixels in an in vivo rabbit and porcine X‐ray fluoroscopy images, outperforming ten classic heatmap and regression methods, achieving state‐of‐the‐art detection results. The proposed method can also be applied to detect other tiny surgical instruments such as stents and balloons, while preserving the flexibility of the guidewire bending angle.

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“…ARDIOVASCULAR diseases (CVDs) are diseases of the heart or its blood vessels. Despite the recent constantly reduced age-standardized CVD morbidity rates, CVD is still the highest contributor of global mortality [1]. Recent studies show that less invasive surgery can aid intervention of CVDs.…”

Section: Introductionmentioning

confidence: 99%

Exploring Operators’ Natural Behaviors to Predict Catheterization Trial Outcomes in Robot-Assisted Intravascular Interventions

Omisore

Duan

et al. 2022

IEEE Trans. Med. Robot. Bionics

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Recently, robotic catheterization has enhanced the outcomes of cardiovascular interventions. Meanwhile, the roles of operator's natural behavior in the robot-assisted intravascular procedures need more attention. In this paper, operators' hand activities related to endovascular tool manipulation are studied to explore how operators' hand motions aid robotic catheterization. Controlled in-vivo studies were set up to acquire four types of operators' natural behaviors during 60 robotic catheterization trials, and activity signals were recorded to proxy operators' skills. A multi-layer recognition model is developed for predicting trial outcomes and recognizing the hand motions made during the procedures. The model operates convolution and dense layers to multiplex features extracted in single to multiple data modalities. Starting with initial-decision layer, the model is built to train and classify catheterization trials recorded from nine interventionists as successful and unsuccessful. Next, a motiondecision layer is built to recognize interventionists' hand motions using features from different data modalities. Lastly, a mixeddecision layer is integrated to recognize the motion patterns of the successful and unsuccessful trials. Results show that the initial-decision layer has an accuracy of 99.44% in predicting the catheterization trials as successful and unsuccessful, while the motion-decision layer shows accuracies of 98.55% and 98.44% in classifying the seven types of hand motions that operators engage during successful and unsuccessful trials, respectively. Also, the mixed-decision layer has an accuracy of 93.96% in recognizing 14 mixed patterns from both trial classes. This study provides an objective template for skill training and evaluation in robotassisted catheterization.

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Cohort Profile: LIFE course study in CARdiovascular disease Epidemiology (LIFECARE)

Cited by 5 publications

References 18 publications

Prevalence of Metabolic Disease among Adults in Indonesia: A Cross-Sectional Population-Based Survey

Prevalence of Metabolic Disease among Adults in Indonesia: A Cross-Sectional Population-Based Survey

Guidewire Endpoint Detection Based on Pixel‐Adjacent Relation during Robot‐Assisted Intravascular Catheterization: In Vivo Mammalian Models

Exploring Operators’ Natural Behaviors to Predict Catheterization Trial Outcomes in Robot-Assisted Intravascular Interventions

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