PDF(Pubmed)
Abstract：
BACKGROUND: Dengue epidemics impose considerable strain on healthcare resources. Real-time continuous and non-invasive monitoring of patients admitted to the hospital could lead to improved care and outcomes. We evaluated the performance of a commercially available wearable (SmartCare) utilising photoplethysmography (PPG) to stratify clinical risk for a cohort of hospitalised patients with dengue in Vietnam.
METHODS: We performed a prospective observational study for adult and paediatric patients with a clinical diagnosis of dengue at the Hospital for Tropical Disease, Ho Chi Minh City, Vietnam. Patients underwent PPG monitoring early during admission alongside standard clinical care. PPG waveforms were analysed using machine learning models. Adult patients were classified between 3 severity classes: i) uncomplicated (ward-based), ii) moderate-severe (emergency department-based), and iii) severe (ICU-based). Data from paediatric patients were split into 2 classes: i) severe (during ICU stay) and ii) follow-up (14-21 days after the illness onset). Model performances were evaluated using standard classification metrics and 5-fold stratified cross-validation.
RESULTS: We included PPG and clinical data from 132 adults and 15 paediatric patients with a median age of 28 (IQR, 21-35) and 12 (IQR, 9-13) years respectively. 1781 h of PPG data were available for analysis. The best performing convolutional neural network models (CNN) achieved a precision of 0.785 and recall of 0.771 in classifying adult patients according to severity class and a precision of 0.891 and recall of 0.891 in classifying between disease and post-disease state in paediatric patients.
CONCLUSIONS: We demonstrate that the use of a low-cost wearable provided clinically actionable data to differentiate between patients with dengue of varying severity. Continuous monitoring and connectivity to early warning systems could significantly benefit clinical care in dengue, particularly within an endemic setting. Work is currently underway to implement these models for dynamic risk predictions and assist in individualised patient care.
BACKGROUND: EPSRC Centre for Doctoral Training in High-Performance Embedded and Distributed Systems (HiPEDS) (Grant: EP/L016796/1) and the Wellcome Trust (Grants: 215010/Z/18/Z and 215688/Z/19/Z).
METHODS: We performed a prospective observational study for adult and paediatric patients with a clinical diagnosis of dengue at the Hospital for Tropical Disease, Ho Chi Minh City, Vietnam. Patients underwent PPG monitoring early during admission alongside standard clinical care. PPG waveforms were analysed using machine learning models. Adult patients were classified between 3 severity classes: i) uncomplicated (ward-based), ii) moderate-severe (emergency department-based), and iii) severe (ICU-based). Data from paediatric patients were split into 2 classes: i) severe (during ICU stay) and ii) follow-up (14-21 days after the illness onset). Model performances were evaluated using standard classification metrics and 5-fold stratified cross-validation.
RESULTS: We included PPG and clinical data from 132 adults and 15 paediatric patients with a median age of 28 (IQR, 21-35) and 12 (IQR, 9-13) years respectively. 1781 h of PPG data were available for analysis. The best performing convolutional neural network models (CNN) achieved a precision of 0.785 and recall of 0.771 in classifying adult patients according to severity class and a precision of 0.891 and recall of 0.891 in classifying between disease and post-disease state in paediatric patients.
CONCLUSIONS: We demonstrate that the use of a low-cost wearable provided clinically actionable data to differentiate between patients with dengue of varying severity. Continuous monitoring and connectivity to early warning systems could significantly benefit clinical care in dengue, particularly within an endemic setting. Work is currently underway to implement these models for dynamic risk predictions and assist in individualised patient care.
BACKGROUND: EPSRC Centre for Doctoral Training in High-Performance Embedded and Distributed Systems (HiPEDS) (Grant: EP/L016796/1) and the Wellcome Trust (Grants: 215010/Z/18/Z and 215688/Z/19/Z).
摘要：
背景：登革热流行给医疗资源带来了相当大的压力。对入院患者的实时连续和非侵入性监测可以改善护理和结果。我们评估了市售可穿戴式（SmartCare）利用光体积描记术（PPG）对越南登革热住院患者队列的临床风险进行分层的性能。
方法：我们在热带病医院对临床诊断为登革热的成人和儿科患者进行了一项前瞻性观察研究，胡志明市,越南。患者在入院早期接受PPG监测以及标准临床护理。使用机器学习模型分析PPG波形。成年患者分为3种严重程度类别：i）无并发症（以病房为基础），ii)中度-重度(以急诊科为基础)，和iii)严重(基于ICU)。儿科患者的数据分为2类：i）严重（ICU住院期间）和ii）随访（发病后14-21天）。使用标准分类指标和5倍分层交叉验证评估模型性能。
结果：我们纳入了132名成年人和15名儿科患者的PPG和临床数据，中位年龄为28岁（IQR，21-35）和12（IQR，分别为9-13）年。1781小时的PPG数据可用于分析。表现最好的卷积神经网络模型（CNN）在根据严重程度等级对成年患者进行分类时实现了0.785的精度和0.771的召回率，在对疾病和疾病后状态进行分类时实现了0.891的精度和0.891的召回率。儿科患者。
结论：我们证明，使用低成本可穿戴设备提供了临床可操作的数据来区分不同严重程度的登革热患者。持续的监测和与早期预警系统的连接可以大大有利于登革热的临床护理，特别是在地方性环境中。目前正在实施这些模型以进行动态风险预测并协助个性化患者护理的工作。
背景：EPSRC高性能嵌入式和分布式系统（HiPEDS）博士培训中心（授予：EP/L016796/1）和WellcomeTrust（授予：215010/Z/18/Z和215688/Z/19/Z）。
方法：我们在热带病医院对临床诊断为登革热的成人和儿科患者进行了一项前瞻性观察研究，胡志明市,越南。患者在入院早期接受PPG监测以及标准临床护理。使用机器学习模型分析PPG波形。成年患者分为3种严重程度类别：i）无并发症（以病房为基础），ii)中度-重度(以急诊科为基础)，和iii)严重(基于ICU)。儿科患者的数据分为2类：i）严重（ICU住院期间）和ii）随访（发病后14-21天）。使用标准分类指标和5倍分层交叉验证评估模型性能。
结果：我们纳入了132名成年人和15名儿科患者的PPG和临床数据，中位年龄为28岁（IQR，21-35）和12（IQR，分别为9-13）年。1781小时的PPG数据可用于分析。表现最好的卷积神经网络模型（CNN）在根据严重程度等级对成年患者进行分类时实现了0.785的精度和0.771的召回率，在对疾病和疾病后状态进行分类时实现了0.891的精度和0.891的召回率。儿科患者。
结论：我们证明，使用低成本可穿戴设备提供了临床可操作的数据来区分不同严重程度的登革热患者。持续的监测和与早期预警系统的连接可以大大有利于登革热的临床护理，特别是在地方性环境中。目前正在实施这些模型以进行动态风险预测并协助个性化患者护理的工作。
背景：EPSRC高性能嵌入式和分布式系统（HiPEDS）博士培训中心（授予：EP/L016796/1）和WellcomeTrust（授予：215010/Z/18/Z和215688/Z/19/Z）。
