Development of a Pediatric Acute Asthma Prediction Rule for severity and outcome

制定儿科急性哮喘严重程度和结果预测规则

• 批准号: 7585292
• 负责人: DONALD Hayes ARNOLD
• 金额: $ 14.77万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-03-15 至 2013-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7585292
• 关键词: Accident and Emergency department; Acute; Admission activity; Adolescent; Age; Agonist; Airway Resistance; Asthma; Caring; Child; Childhood; Chronic Disease; Classification; Clinical; Data; Data Element; Development; Disease Management; Enrollment; Future; Home environment; Hospitals; Hour; Length of Stay; Mathematics; Measures; Medical; Modeling; Nature; Nurses; Outcome; Participant; Patient Care; Persons; Physical Examination; Physicians; Physiologic pulse; Physiological; Prospective Studies; Pulmonary function tests; Relapse; Risk Factors; Sensitivity and Specificity; Severities; Severity of illness; Spirometry; Study Subject; System; Testing; Therapeutic; Time; Validation; Validity and Reliability; abstracting; airway obstruction; data integration; improved; member; novel; respiratory; response; trend

DESCRIPTION (provided by applicant): We propose to develop a multivariable Asthma Prediction Rule (APR) conforming to clinical and biostatistical standards for clinical prediction rule development. The APR will include physical findings, vital signs, and objective physiologic variables obtained at presentation to an acute care facility, and trends of this data over the first 6 hours of care. This data is gathered by multiple members of a medical team, but is infrequently utilized in a coherent and systematic manner to optimally advance patient care. The APR will use those elements of data that are statistically demonstrated to predict the need for in-hospital care. In addition, we will further develop and validate a mathematic model using physiologic waveform data from the pulse oximeter as a candidate predictor variable for the APR. The novel integration of this data into the APR might enhance the predictive validity and reliability of the APR to be developed. Primary Aim: To determine independent risk factors in persons with asthma exacerbations that are associated with the need for in-hospital care. We hypothesize that select predictor variables can be utilized in an APR that will in turn predict need for in-hospital care with sufficient sensitivity and specificity to objectively inform clinical decisions. To test this hypothesis we will conduct a prospective study of children ages 7-17 years who present to a Pediatric Emergency Department with asthma exacerbations.Physical examination findings, vital signs, and objective physiologic variables obtained at presentation and during the first 6 hours of care will be used as candidate predictor variables to predict either hospital length of stay greater than 1 day if the participant is admitted to hospital, or relapse within 48 hours if discharged to home. This data will be utilized for the development of an APR in accordance with established clinical and biostatistical standards. Secondary Aim: To further develop and validate a real-time, continuous asthma severity measure using quantified oximeter plethysmograph waveform data that can be used and tested simultaneously as a candidate predictor variable for the APR. We hypothesize that an established mathematic model for oximeter Plethysmograph Estimated Pulsus paradoxus (PEP) will provide an effort-independent, real-time, continuous and valid estimate of the severity of airway obstruction for incorporation in the APR. To test this hypothesis, PEP will be statistically compared with the criterion standards of spirometry and specific airway resistance. (End of Abstract)

描述（由申请人提供）：我们建议开发符合临床预测规则开发的临床和生物统计学标准的多变量哮喘预测规则（APR）。APR将包括在急诊护理机构就诊时获得的体格检查结果、生命体征和客观生理变量，以及该数据在护理前6小时内的趋势。这些数据由医疗团队的多名成员收集，但很少以连贯和系统的方式用于最佳地推进患者护理。APR将使用统计学证明的数据元素来预测住院护理的需求。此外，我们将进一步开发和验证数学模型，使用脉搏血氧仪的生理波形数据作为APR的候选预测变量。将该数据整合到APR中的新方法可能会增强待开发APR的预测有效性和可靠性。主要目的：确定哮喘急性发作患者与住院治疗需求相关的独立危险因素。我们假设，选择预测变量可以在APR中使用，这反过来又可以预测住院护理的需要，具有足够的灵敏度和特异性，以客观地为临床决策提供信息。为了验证这一假设，我们将对7-17岁的儿童进行前瞻性研究，这些儿童因哮喘急性发作而到儿科急诊科就诊。在就诊时和最初6小时护理期间获得的客观生理变量将用作候选预测变量，以预测如果受试者入院，住院，或出院回家后48小时内复发。根据既定的临床和生物统计学标准，这些数据将用于开发APR。次要目的：为了进一步开发和验证使用定量血氧体积描记波形数据的实时、连续的哮喘严重程度测量，该数据可以作为APR的候选预测变量同时使用和测试。我们假设，血氧体积描记估计的奇异脉搏（PEP）的已建立数学模型将提供与努力无关的实时、为了检验这一假设，PEP将与肺量测定和比气道阻力的标准进行统计学比较。(End摘要）