Multi-Site Validation Study of the HLHS Arrest Predictor

HLHS 逮捕预测器的多站点验证研究

基本信息

批准号：
10418753
负责人：
Craig G Rusin
金额：
$ 58.38万
依托单位：
BAYLOR COLLEGE OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-01 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10418753
关键词：
Acute Adult Algorithms Anatomy Area Arrhythmia Behavior Blood flow Cardiac Cardiac Surgery procedures Caring Cause of Death Cessation of life Child Clinical Colorado Common Ventricle Computational algorithm Computers Critically ill children Data Detection Diagnosis Disease Early Intervention Equipment Event Future Goals Heart Heart Arrest Heart Ventricle Hospitalization Hospitals Hour Infant Lesion Life Logistic Regressions Lung Measures Modeling Monitor Morbidity - disease rate Multicenter Studies Necrotizing Enterocolitis Nurses Operative Surgical Procedures Outcome Palliative Surgery Patient Monitoring Patient Monitoring System Patients Pattern Pediatric Hospitals Pediatric cardiology Performance Physicians Physiological Physiology Population Postoperative Complications Reporting Research Risk Sensitivity and Specificity Severity of illness Site Surgical complication Symptoms System Techniques Technology Testing Texas Time Validation Work adjudicate base care providers clinical center clinical practice cohort improved improved outcome indexing infancy machine learning algorithm mechanical circulatory support mortality news operation prediction algorithm prevent prospective repaired respiratory validation studies

项目摘要

Each year in the US, there are thousands of children who are born with a severe congenital deformation, where one of the two ventricles in the heart is severely underdeveloped. If they survive infancy, these children can go on to live full and normal lives. The mortality rate for this condition is 15%, and 63% of these deaths are due to cardio-respiratory arrests. This alarming rate of arrest events persists despite vigilant ICU care with the best available intensive monitoring equipment. Our overall goal is to improve current patient monitoring systems by developing machine learning algorithms that can predict the onset of an arrest event, hours before it occurs. This early warning indication can be provided to nurses and doctors who can intervene to prevent these life-threatening events from occurring, improving outcomes for these critically ill children. Preliminary studies at Texas Children's Hospital have resulted in a computer algorithm that can estimate the odds of arrest in single ventricle children, 1-2 hours prior to overt symptoms. The algorithm is based on a logistic regression risk model, and was developed using over 55,000 hours of vital sign observations. The specific aims of the proposed research are: (1) To test the hypothesis that this computer algorithm can provide an early warning of arrest, with sufficient accuracy for clinical use across different clinical centers; (2) To understand the relationship between the risk score provided by this algorithm and other post- surgical complications that commonly occur in these children during the their hospitalization. Aim 1 is a multi-center study of this algorithm on a large, prospective, and independent cohort, in order to measure its true predictive performance. Performance metrics to be measured will be the ROC area and positive and negative likelihood ratios. This will help us determine the optimal threshold for the detection of an arrest event. Aims 2 focus on relating the risk of arrest to outcomes such as mechanical circulatory support, re-operation, arrhythmia, and necrotizing enterocolitis. Successful completion of these aims will result in the first clinically validated, real- time early warning system for anticipating acute arrest events in children with single ventricle physiology. The techniques and technologies developed in this work are immediately translatable to other diseases and conditions for both adults and children.

每年在美国，有成千上万的孩子出生于严重的先天性变形，心脏中两个心室之一的变形严重欠发达。如果他们这些孩子可以继续生存，可以继续过着充实的正常生活。此的死亡率病情为15％，其中63％的死亡是由于心脏呼吸逮捕。这个令人震惊的尽管警惕的ICU护理以及最佳的密集型ICU护理，但逮捕事件的发生率仍然存在监视设备。我们的总体目标是通过开发机器学习算法可以预测逮捕事件的发作，小时在发生之前。可以向可以提供这种预警指示干预以防止这些威胁生命的事件发生，改善了这些事件重病的孩子。德克萨斯儿童医院的初步研究已导致可以估计单个心室儿童逮捕几率的计算机算法，1-2小时在明显症状之前。该算法基于逻辑回归风险模型，并且是使用超过55,000个小时的生命体征观测来开发。提议的具体目的研究是：（1）检验该计算机算法可以提供早期的假设警告逮捕，具有足够的准确性，可在不同的临床中心进行临床使用；（2）至了解该算法提供的风险评分与其他后的风险评分之间的关系这些儿童在住院期间通常发生的手术并发症。 AIM 1是对大型，前瞻性和独立队列的该算法的多中心研究，为了衡量其真正的预测性能。要测量的性能指标将是 ROC面积以及阳性和负有可能性比率。这将帮助我们确定最佳检测逮捕事件的门槛。目标2专注于将逮捕风险与诸如机械循环支持，再操作，心律不齐和坏死的结果小肠结肠炎。这些目标的成功完成将导致第一个经过临床验证的，现实时间预警系统预计有单脑室的儿童急性逮捕事件生理。这项工作中开发的技术和技术立即可翻译成成人和儿童的其他疾病和状况。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Novel Method of Calculating Pulse Pressure Variation to Predict Fluid Responsiveness to Transfusion in Very Low Birth Weight Infants.

DOI：
10.1016/j.jpeds.2021.04.012
发表时间：
2021-07
期刊：
The Journal of pediatrics
影响因子：
0
作者：
Foughty ZC;Tavaslioglu O;Rhee CJ;Elizondo LI;Rusin CG;Penny DJ;Acosta S;Rios DR
通讯作者：
Rios DR

Creatinine filtration kinetics in critically Ill neonates.