Dynamic Critical Congenital Heart Screening With Addition of Perfusion Measurements
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 320
- 试验地点
- 6
- 主要终点
- Area under the curve for receiver operating characteristics for critical congenital heart disease using ML inpatient algorithm.
研究概览
简要总结
The purpose of this study is to implement and externally validate an inpatient ML algorithm that combines pulse oximetry features for critical congenital heart disease (CCHD) screening.
详细描述
The study will externally validate an algorithm that combines non-invasive oxygenation and perfusion measurements as a screening tool for CCHD. In a previous study, the investigators created an algorithm that combines non-invasive measurements of oxygenation and perfusion over at least two measurements using machine learning (ML) techniques. The prior model was created and tested using internal validation (k-fold validation). Thus, the investigators will test the model on an external sample of patients to test generalizability of the model. Additionally, the team will trial a repeated measurement for any "failure" of the screen to assess impact on the false positive rate. Study team will also use repeated pulse oximetry measurements (up to 4 total and including measurements after 48 hours of age, which may be done outpatient) to create a new algorithm that incorporates new data over time. The central hypothesis is that the addition of non-invasive perfusion measurements will be superior to SpO2-alone screening for CCHD detection and a model that incorporates repeated measurements will enhance detection of CCHD while preserving the specificity.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 0 Minutes 至 21 Days（Child）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Age < 22 days
- •Fetuses suspected to have congenital heart disease
- •Newborns with suspected/confirmed critical congenital heart disease
- •Asymptomatic newborn undergoing SpO2 screening for CCHD
排除标准
- •Echocardiogram completed prior to enrollment as the newborn would then no longer be considered "asymptomatic undergoing SpO2 screening for CCHD"
- •For Newborns with confirmed/suspected congenital heart disease (CHD): a) Patent ductus arteriosus and/or atrial septal defect/patent foramen ovale without other defects, b) Corrective cardiac surgical or catheter intervention performed before enrollment or c) Current infusions of vasoactive medications other than prostaglandin therapy.
研究组 & 干预措施
SpO2 and PIx Measurement
Non-invasive measurements of oxygenation (SpO2) and perfusion (PIx) will be measured with pulse oximeters and a ML CCHD screening algorithm will be assigning a prediction every minute.
干预措施: SpO2/PIx Measurement and ML Algorithm (Diagnostic Test)
结局指标
主要结局
Area under the curve for receiver operating characteristics for critical congenital heart disease using ML inpatient algorithm.
时间窗: Through study completion, an average of 4 years
Receiver operating characteristics reflect a combination of sensitivity and specificity of a test. The investigators will identify the true positive and true negative rates for CCHD by confirming health status to a minimum of 2 months of age. The investigators will also utilize birth defect and death registries for missing infants.
次要结局
- Specificity for critical congenital heart disease using ML inpatient algorithm (0-24 hours and 24-48 hours)(Through study completion, an average of 4 years)
- Sensitivity for critical congenital heart disease using ML inpatient algorithm (0-24 hours and 24-48 hours)(Through study completion, an average of 4 years)
- Sensitivity for critical congenital heart disease using dynamic ML algorithm(Through study completion, an average of 4 years)
- Area under the curve for receiver operating characteristics for critical congenital heart disease using dynamic ML algorithm(Through study completion, an average of 4 years)
- Specificity for critical congenital heart disease using dynamic ML model(Through study completion, an average of 4 years)
- Sensitivity for critical coarctation of the aorta using dynamic ML algorithm(Through study completion, an average of 4 years)