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.

项目概要 慢性肾脏病 (CKD) 影响着 14% 的美国人口，并且与这两种疾病的高风险相关 缺血性和出血性中风，死亡率高达普通人群的三倍。 对于 CKD 患者来说，有效的中风风险预测是​​有缺陷的，因为 1) 合并症往往未被诊断出来， 2) 中风风险分层方案不考虑 CKD 的阶段，以及 3) 中风风险变化的检测 由于透析需要实时风险监测，目前尚无法实现。实时监控 CKD 患者的中风风险，特别是接受透析的患者，可能会影响采用 治疗策略并促进更加个性化的治疗方法。更好的护理和预防 通过监测这些高危患者将降低死亡率及其高昂的人均费用，这是 比平均医疗保健支出高出五倍。 Biotricity 正在开发创新系统 Bioflux-AI 用于实时监测和预测 CKD 患者中风发作。 Bioflux-AI 结合了 经 FDA 批准的高精度小型移动心脏遥测 (MCT) 设备，采用人工智能驱动算法 专门针对 4 期和 5 期 CKD 患者中风的预测进行了培训。 Biotricity 之前已生成 以及用于自动检测心电图异常（包括心房颤动 (AF)）的经过验证的算法。 鉴于 AF 与血栓形成风险增加以及 CKD 中缺血性中风的风险增加密切相关 Biotricity 建议将这种心律失常的检测与 CKD 的其他中风危险因素结合起来 患者（年龄、体重、身高、BMI、CKD 状况、糖尿病、心脏病）和心电图参数来预测中风 实时风险。为此，在 SBIR 第一阶段项目中，我们设计了一种卷积神经网络算法，该算法将 将开发、培训和验证纳入所有这些风险因素。本次可行性研究的实现 研究将为基于人工智能的中风预测算法的进一步开发和优化铺平道路 在 CKD 患者中，同时将应用范围扩大到更大的患者群体，验证预测 患有其他慢性疾病的患者的算法。