Abstract
OBJECTIVES: Differences and biases between directly measured intra-arterial blood pressure and intermittingly measured noninvasive blood pressure using an oscillometric cuff method have been reported in adults and children. At the bedside, clinicians are required to assign a confidence to a specific blood pressure measurement before acting upon it, and this is challenging when there is discordance between measurement techniques. We hypothesized that big data could define and quantify the relationship between noninvasive blood pressure and intra-arterial blood pressure measurements and how they can be influenced by patient characteristics, thereby aiding bedside decision-making. DESIGN: A retrospective analysis of cuff blood pressure readings with associated concurrent invasive arterial blood pressure measurements (452,195 noninvasive blood pressure measurements). SETTING: Critical care unit at The Hospital for Sick Children, Toronto. PATIENTS: Six-thousand two-hundred ninety-seven patients less than or equal to 18 years old, hospitalized in a critical care unit with an indwelling arterial line. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Two-dimensional distributions of intra-arterial blood pressure and noninvasive blood pressure were generated and the conditional distributions of intra-arterial blood pressure examined as a function of the noninvasive systolic, diastolic, or mean blood pressure. Modification of these distributions according to age and gender were examined using a multilevel mixed-effects model. For any given combination of patient age and noninvasive blood pressure, the expected distribution of intra-arterial blood pressure readings exhibited marked variability at the population level and a bias that significantly depended on the noninvasive blood pressure value and age. We developed an online tool that allows exploration of the relationship between noninvasive blood pressure and intra-arterial blood pressure and the conditional probability distributions according to age. CONCLUSIONS: A large physiologic dataset provides clinically applicable insights into the relationship between noninvasive blood pressure and intra-arterial blood pressure measurements that can help guide decision-making at the patient bedside.