AI-enabled Stroke Prediction in Patients with Chronic Kidney Disease

基于人工智能的慢性肾病患者中风预测

• 批准号: 10481070
• 负责人: Waqaas Al-Siddiq
• 金额: $ 24.01万
• 依托单位: BIOTRICITY INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10481070
• 关键词: Adopted; Affect; Age; Algorithms; Ambulatory Monitoring; Anticoagulant therapy; Aortic Valve Stenosis; Arrhythmia; Artificial Intelligence; Atrial Fibrillation; Blood coagulation; Body mass index; Brain hemorrhage; Businesses; Cardiac; Cardiovascular Diseases; Caring; Chronic Care; Chronic Disease; Chronic Kidney Failure; Clinical; Computers; Custom; Data; Data Set; Detection; Development; Devices; Diabetes Mellitus; Dialysis procedure; Electrocardiogram; End stage renal failure; Engineering; FDA approved; Feasibility Studies; General Population; Healthcare; Heart Diseases; Heart failure; Height; Hemorrhage; Hypertension; Institutes; Ischemic Stroke; Lead; Liquid substance; Machine Learning; Measurement; Medical Device; Monitor; Nephrology; Outpatients; Pathway Analysis; Patients; Pattern; Phase; Population; Prevention; Prevention strategy; Real-Time Systems; Risk; Risk Factors; Running; Scheme; Schools; Sensitivity and Specificity; Signal Transduction; Small Business Innovation Research Grant; Specialist; Stroke; System; Technology; Telemetry; Therapeutic; Time; Training; United States National Institutes of Health; Validation; Variant; Weight; Weight Gain; algorithm training; artificial neural network; automated algorithm; base; clinical practice; commercialization; comorbidity; convolutional neural network; cost; data integrity; detection platform; experience; high risk; innovation; mortality; multidisciplinary; neural network algorithm; personalized approach; personalized therapeutic; phase 1 study; portability; post stroke; prediction algorithm; preventive intervention; real time monitoring; remote monitoring; risk prediction; risk stratification; skills; stroke risk

PROJECT SUMMARY Chronic Kidney Disease (CKD) affects 14% of the U.S. population and is associated with a high risk of both ischemic and hemorrhagic strokes and a mortality rate of up to three times that of the general population. Effective stroke risk prediction is flawed in CKD patients, because 1) comorbidities often remain undiagnosed, 2) stroke risk stratification schemes do not consider stages of CKD, and 3) the detection of stroke risk variations due to dialysis requires real-time risk monitoring, which is unavailable to date. The real-time monitoring of the stroke risk in CKD patients, and in particular for those undergoing dialysis, is likely to influence adopted therapeutic strategies and promote a more personalized therapeutic approach. Better care and prevention through monitoring of these high-risk patients will reduce mortality rates and their high per capita cost, which is five times higher than the average healthcare spending. Biotricity is developing Bioflux-AI, an innovative system for real-time monitoring and prediction of stroke episodes in CKD patients. Bioflux-AI combines an FDA-approved, high-precision, small mobile cardiac telemetry (MCT) device with AI-driven algorithms specifically trained for the prediction of stroke in stage 4 and 5 CKD patients. Biotricity has previously generated and validated algorithms for the automated detection of ECG abnormalities, including Atrial Fibrillation (AF). Given the strong association of AF with increased risk of blood clot formation and hence, ischemic stroke in CKD patients, Biotricity proposes to combine the detection of this arrhythmia with other stroke risk factors of CKD patients (age, weight, height, BMI, CKD status, diabetes, heart disease) and ECG parameters to predict stroke risk in real-time. To this aim, in this SBIR Phase I project a convolutional neural network algorithm, which will incorporate all these risk factors, will be developed, trained and validated. The accomplishment of this feasibility study will pave the road for further development and optimization of the AI-based algorithm for stroke prediction in CKD patients, while widening the application to a larger patient demographic, validating the predictive algorithm for patients with other chronic diseases.

项目摘要 慢性肾脏疾病（CKD）影响美国人群的14％，并且与两者的高风险有关 缺血性和出血性中风，死亡率最高三倍。 CKD患者存在有效的中风风险预测是​​有缺陷的，因为1）合并症通常仍未诊断， 2）中风风险分层方案不考虑CKD的阶段，3）检测中风风险变化 由于透析，需要实时风险监控，这是迄今为止无法使用的。实时监控 CKD患者的中风风险，尤其是患有透析的患者，可能会影响所采用的 治疗策略并促进更个性化的治疗方法。更好的护理和预防 通过监测这些高风险患者将降低死亡率及其人均成本高，是 比平均医疗保健支出高五倍。生物学正在开发Bioflux-AI，这是一种创新系统 为了实时监测和预测CKD患者的中风发作。 BioFlux-ai结合了AN 具有AI驱动算法的FDA批准，高精度，小型移动心脏遥测（MCT）设备 专门训练了在第4阶段和5级CKD患者中预测中风的预测。以前已经产生了生物性 并经过验证的算法，用于自动检测ECG异常，包括心房颤动（AF）。 鉴于AF与血凝块形成的风险增加，因此CKD中的缺血性中风 患者，生物学建议将这种心律不齐的检测与CKD的其他中风风险因素结合起来 患者（年龄，体重，身高，BMI，CKD状态，糖尿病，心脏病）和ECG参数可预测中风 实时风险。为此，在SBIR I期中 将所有这些风险因素纳入将得到开发，培训和验证。实现这一可行性 研究将铺平道路，以进一步开发和优化基于AI的中风预测的算法 在CKD患者中，同时扩大了对较大患者人群的应用，以验证预测性 其他慢性疾病患者的算法。