AI-enabled Stroke Prediction in Patients with Chronic Kidney Disease

项目摘要 慢性肾脏病（CKD）影响14%的美国人口，并与两者的高风险相关 缺血性和出血性中风，死亡率高达普通人群的三倍。 有效的中风风险预测在CKD患者中是有缺陷的，因为1）合并症通常未被诊断， 2)中风风险分层方案不考虑CKD的阶段，以及3）检测中风风险变化 由于透析需要实时风险监测，这是迄今为止无法实现的。的实时监控 CKD患者的卒中风险，尤其是接受透析的患者，可能会影响采用 治疗策略和促进更个性化的治疗方法。更好的护理和预防 通过监测这些高危患者将降低死亡率和他们的高人均成本， 是平均医疗支出的五倍。Biotricity正在开发创新系统Bioflux-AI 用于实时监测和预测CKD患者的卒中发作。Bioflux-AI结合了 FDA批准的高精度小型移动的心脏遥测（MCT）设备，采用AI驱动算法 专门接受过预测4期和5期CKD患者卒中的培训。Biotricity之前已经生成了 以及用于自动检测ECG异常（包括房颤（AF））的经过验证的算法。 鉴于房颤与CKD患者血凝块形成风险增加以及缺血性卒中风险增加密切相关， Biotricity建议将这种心律失常的检测与CKD的其他卒中风险因素结合起来 患者（年龄、体重、身高、BMI、CKD状态、糖尿病、心脏病）和预测卒中的ECG参数 实时风险为此，在SBIR第一阶段项目中，卷积神经网络算法将 将制定、培训和验证纳入所有这些风险因素的标准。这种可行性的实现 这项研究将为进一步开发和优化基于AI的中风预测算法铺平道路 在CKD患者中，同时将应用范围扩大到更大的患者人群， 其他慢性病患者的算法。

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.