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.
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