Dr. Arnold's early contributions to science included studies to examine whether mathematic information in the pulse oximeter plethysmograph waveform can be used to measure pulsus paradoxus non-invasively and in real-time. If so, plethysmograph-estimate of pulsus paradoxus (PEP) will be a significant contribution to science and technology because it will provide clinicians an objective metric of acute exacerbation severity and response to treatment. He first studied healthy young adult participants who performed tidal-breathing through an airway circuit with adjustable inspiratory and expiratory resistance valves to generate pulsus paradoxus. PEP was calculated using a dedicated microprocessor and software program, based on change of plethysmograph waveform indices during the respiratory cycle. PEP predicted the degree of applied airway resistance. He has subsequently reported the validation of PEP using the criterion standards %-predicted FEV1 and airway resistance in the pulmonary function lab and in the emergency department with COPD and asthma. Assessment of acute asthma exacerbation severity directs treatment and the decision whether to hospitalize a patient, yet there are few objective measures to assess severity and predict outcome. His scientific contributions in this area have included work to improve assessment of acute exacerbations and to decrease variability of assessment between clinicians. These studies have examined the use of patient characteristics and clinical variables that are readily available at the bedside. He has demonstrated that select characteristics and variables can be used to accurately measure exacerbation severity and response to treatment with high inter-rater reliability. There are limited tools available to clinicians to inform hospitalization decisions for pediatric patients with acute asthma exacerbations. The scientific contributions of his team have included development and validation of the Asthma Prediction Rule (APR). This included development of both a comprehensive 15-variable and a reduced-form 5-variable prediction model for electronic decision-support. The APR might provide clinical decision-support to decrease unnecessary treatment variability, improve resource utilization, and improve patient outcomes measured using need-for-hospitalization criteria.

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