A new paradigm of ‘real time’ stroke risk prediction and integrated care management in the digital health era

Clinical outcomes of patients with atrial fibrillation (AF) can be improved by an integrated care approach. We analyzed whether adherence with the AF Better Care (ABC) pathway for integrated care management would reduce the risk of dementia in a nationwide AF cohort. Using the National Health Insurance Service database of Korea, 228,026 non-valvular AF patients were retrospectively evaluated between 2005 and 2015. Patients meeting all criteria of the ABC pathway were classified as the “ABC” group and those not classified as the “non-ABC” group. During a median (25th, 75th percentiles) follow-up of 6.0 (3.3, 9.5) years, the ABC group had lower rates and risk of overall dementia (0.17 vs. 1.11 per 100 person-years, p < 0.001; hazard ratio (HR) 0.80; 95% CI 0.73–0.87) and both Alzheimer’s (HR 0.79, 95% CI 0.71–0.88) and vascular dementia (HR 0.76, 95% CI 0.59–0.98) than the non-ABC group. The stratified analysis showed that the ABC pathway reduced the risk of dementia regardless of sex, comorbidities, and in patients with high stroke risk. Adherence with the ABC pathway is associated with a reduced risk of dementia in AF patients. Due to the high medical burden of AF, it is necessary to implement integrated AF management to reduce the risk of dementia.

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A Study on the National Health Insurance Service Staff’ Recognitions of the Integrated Care Management System for the Elderly

The Korean Journal of Political Science ◽

10.34221/kjps.2017.25.3.1 ◽

2017 ◽

Vol 25 (3) ◽

pp. 1-23

Author(s):

Sung-Jin Nam ◽

Jung-Ho Choi ◽

Jae-Mo Lee

Keyword(s):

Health Insurance ◽

Integrated Care ◽

National Health Insurance ◽

National Health ◽

Management System ◽

Care Management ◽

The Elderly ◽

Service Staff

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IoT-Based Patient Movement Monitoring: The Post-Operative Hip Fracture Rehabilitation Model

Future Internet ◽

10.3390/fi13080195 ◽

2021 ◽

Vol 13 (8) ◽

pp. 195

Author(s):

Akash Gupta ◽

Adnan Al-Anbuky

Keyword(s):

Hip Fracture ◽

Real Time ◽

System Performance ◽

Digital Health ◽

Time Behavior ◽

Functional Requirements ◽

Rehabilitation Process ◽

Rehabilitation Model ◽

The Impact ◽

Movement Monitoring

Hip fracture incidence is life-threatening and has an impact on the person’s physical functionality and their ability to live independently. Proper rehabilitation with a set program can play a significant role in recovering the person’s physical mobility, boosting their quality of life, reducing adverse clinical outcomes, and shortening hospital stays. The Internet of Things (IoT), with advancements in digital health, could be leveraged to enhance the backup intelligence used in the rehabilitation process and provide transparent coordination and information about movement during activities among relevant parties. This paper presents a post-operative hip fracture rehabilitation model that clarifies the involved rehabilitation process, its associated events, and the main physical movements of interest across all stages of care. To support this model, the paper proposes an IoT-enabled movement monitoring system architecture. The architecture reflects the key operational functionalities required to monitor patients in real time and throughout the rehabilitation process. The approach was tested incrementally on ten healthy subjects, particularly for factors relevant to the recognition and tracking of movements of interest. The analysis reflects the significance of personalization and the significance of a one-minute history of data in monitoring the real-time behavior. This paper also looks at the impact of edge computing at the gateway and a wearable sensor edge on system performance. The approach provides a solution for an architecture that balances system performance with remote monitoring functional requirements.

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COATED-PLATELETS ENHANCE TIA/STROKE RISK PREDICTION IN ASYMPTOMATIC CAROTID STENOSIS PATIENTS FOLLOWED WITH CAROTID DOPPLER SURVEILLANCE

Journal of the American College of Cardiology ◽

10.1016/s0735-1097(21)03173-9 ◽

2021 ◽

Vol 77 (18) ◽

pp. 1817

Author(s):

Mohamad Khattab ◽

Arleen Ramirez-Jimenez ◽

Andrea Vincent ◽

George Dale ◽

Calin Prodan ◽

...

Keyword(s):

Carotid Stenosis ◽

Risk Prediction ◽

Stroke Risk ◽

Asymptomatic Carotid Stenosis ◽

Asymptomatic Carotid ◽

Carotid Doppler

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Development of an Institution-Specific Readmission Risk Prediction Model for Real-time Prediction and Patient-Centered Interventions

Journal of General Internal Medicine ◽

10.1007/s11606-020-06549-9 ◽

2021 ◽

Author(s):

Ann-Marcia C. Tukpah ◽

Eric Cawi ◽

Laurie Wolf ◽

Arye Nehorai ◽

Lenise Cummings-Vaughn

Keyword(s):

Prediction Model ◽

Real Time ◽

Risk Prediction ◽

Risk Prediction Model ◽

Patient Centered ◽

Time Prediction ◽

Readmission Risk

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Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction

Sensors ◽

10.3390/s21020460 ◽

2021 ◽

Vol 21 (2) ◽

pp. 460

Author(s):

Yun-Hsuan Chen ◽

Mohamad Sawan

Keyword(s):

Risk Prediction ◽

High Precision ◽

Near Infrared ◽

Stroke Risk ◽

Functional Near Infrared Spectroscopy ◽

Frequency Of Use ◽

Data Continuity ◽

User Friendly ◽

Different Characteristics ◽

The Internet Of Things

We review in this paper the wearable-based technologies intended for real-time monitoring of stroke-related physiological parameters. These measurements are undertaken to prevent death and disability due to stroke. We compare the various characteristics, such as weight, accessibility, frequency of use, data continuity, and response time of these wearables. It was found that the most user-friendly wearables can have limitations in reporting high-precision prediction outcomes. Therefore, we report also the trend of integrating these wearables into the internet of things (IoT) and combining electronic health records (EHRs) and machine learning (ML) algorithms to establish a stroke risk prediction system. Due to different characteristics, such as accessibility, time, and spatial resolution of various wearable-based technologies, strategies of applying different types of wearables to maximize the efficacy of stroke risk prediction are also reported. In addition, based on the various applications of multimodal electroencephalography–functional near-infrared spectroscopy (EEG–fNIRS) on stroke patients, the perspective of using this technique to improve the prediction performance is elaborated. Expected prediction has to be dynamically delivered with high-precision outcomes. There is a need for stroke risk stratification and management to reduce the resulting social and economic burden.

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A Real-time Risk-Prediction Model for Pediatric Venous Thromboembolic Events

PEDIATRICS ◽

10.1542/peds.2020-042325 ◽

2021 ◽

pp. e2020042325

Author(s):

Shannon C. Walker ◽

C. Buddy Creech ◽

Henry J. Domenico ◽

Benjamin French ◽

Daniel W. Byrne ◽

...

Keyword(s):

Prediction Model ◽

Real Time ◽

Risk Prediction ◽

Risk Prediction Model ◽

Thromboembolic Events ◽

Venous Thromboembolic Events ◽

Venous Thromboembolic

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Abstract 17979: Left Atrial Cross Sectional Area is a Novel Echocardiographic Measure Which Improves the Predictive Value of Chads2 Score in Risk Stratification of Cardioembolic Strokes

Circulation ◽

10.1161/circ.130.suppl_2.17979 ◽

2014 ◽

Vol 130 (suppl_2) ◽

Author(s):

Timothy C Tan ◽

Mark Handschumacher ◽

Octavio M Pontes-Neto ◽

Maria C Nunes ◽

Yong H Park ◽

...

Keyword(s):

Risk Stratification ◽

Risk Prediction ◽

Left Atrial ◽

Stroke Risk ◽

Thrombus Formation ◽

Cross Sectional Area ◽

Sectional Area ◽

Chads2 Score ◽

Cross Sectional ◽

C Statistic

Background: Cardioembolic (CE) stroke carries significant morbidity and mortality. Current risk stratification tools such as CHADS2 score do not include any imaging parameters and are based on clinical features, which have limitations. Left atrial (LA) enlargement and remodeling may be associated with CE risk due to predisposition for atrial arrhythmias and thrombus formation. Left atrial cross sectional area (LACSA), a novel echo measure which reflects both LA size and shape, may improve CE stroke risk assessment. Aim: This study examined the value of LACSA in predicting CE stroke risk and the improvement in risk prediction when added to CHADS2 score. Methods: Clinical and echo parameters were examined in a prospective cohort of 1275 consecutive patients with ischemic stroke. Strokes were classified using the Causative Classification of Strokes and 259 (20%) were classified as CE stroke. LACSA was calculated using the formula: π/4*largest measured LA diameter*smallest measured LA diameter where mid LA diameter was measured in the parasternal long axis, 4 chamber and 2 chamber views. Results: Patients with CE stroke had greater LACSA (8.6 ± 2.3 vs 6.4 ± 1.8 cm2/m2; p<0.001) and mean CHADS2 score (2.25 ± 1.28 vs 1.87 ± 1.40; p<0.0001) compared to non-CE stroke patients. LACSA was independently associated with CE strokes (OR 1.21; 95% CI 1.08-1.34; p=0.001) in a multivariable model adjusted for CHADS2, gender, score, BMI, atrial fibrillation, anti-platelet and anti-coagulant use, E/E’ and LVEF. The addition of LACSA to CHADS2 score improved the prediction of CE stroke (c-statistic for predicting CE stroke using CHADS2 alone was 0.59 (95% CI 0.55-0.63) vs CHADS2 and LACSA 0.78 (95% CI 0.72-0.80) (p<0.001). Conclusion: LACSA is a novel measure of LA remodeling and associated with CE stroke. LACSA, an imaging parameter, enhances the risk prediction of the CHADS2 score, a clinical measure of risk, improving risk stratification for CE stroke and impacting therapeutic strategies.

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