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: 7385445
• 负责人: DONALD Hayes ARNOLD
• 金额: $ 14.69万
• 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/7385445
• 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; Pulse taking; Range; Relapse; Risk Factors; Score; Sensitivity and Specificity; Severities; Severity of illness; Spirometry; Standards of Weights and Measures; Study Subject; System; Testing; Therapeutic; Time; Validation; Validity and Reliability; abstracting; airway obstruction; data integration; day; 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）：我们建议制定符合临床和生物统计学标准的多变量哮喘预测规则（APR），用于临床预测规则的制定。APR将包括身体检查、生命体征和到急症护理机构就诊时获得的客观生理变量，以及这些数据在治疗前6小时内的趋势。这些数据是由医疗团队的多个成员收集的，但很少以连贯和系统的方式使用，以最佳地推进患者护理。APR将使用那些经统计证明的数据元素来预测对住院护理的需求。此外，我们将进一步开发和验证一个数学模型，使用脉搏血氧仪的生理波形数据作为APR的候选预测变量。将这些数据整合到APR中可能会提高待开发的APR预测的有效性和可靠性。主要目的：确定与住院治疗需求相关的哮喘加重患者的独立危险因素。我们假设选择的预测变量可以在APR中使用，从而以足够的敏感性和特异性预测住院治疗需求，从而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岁的儿童进行一项前瞻性研究，这些儿童因哮喘发作而就诊于儿科急诊科。体检结果、生命体征和就诊时及治疗前6小时获得的客观生理变量将作为候选预测变量，用于预测住院时间超过1天（如果参与者入院）或出院后48小时内复发（如果参与者出院回家）。这些数据将用于根据既定的临床和生物统计标准制定APR。次要目标:为了进一步开发和验证一种实时的、连续的哮喘严重程度测量方法，该方法使用定量血氧计容积脉搏波波形数据，可以同时作为apr的候选预测变量进行测试。我们假设，血氧计容积脉搏波估计（PEP）的建立数学模型将提供一种不依赖于努力的、实时的、为了验证这一假设，PEP将与肺量测定和特定气道阻力的标准进行统计比较。（摘要结束）