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Published on: July 4, 2018
Distributions and Reference Ranges for Automated Pupillometer Values in Neurocritical Care Patients
Bethany L Lussier1, Sonja E Stutzman, Folefac Atem
1Bethany L. Lussier, MD, is Assistant Professor, Department of Neurology & Neurotherapeutics, UT Southwestern, Dallas, TX. Sonja E. Stutzman, PhD, is Research Manager, Department of Neurology & Neurotherapeutics, UT Southwestern, Dallas, TX. Folefac Atem, PhD, is Assistant Professor, Department of Biostatistics & Data Sciences, University of Texas Health Science Center, Dallas, TX. Aardhra M. Venkatachalam, MPH, is Data Specialist, Department of Biostatistics & Data Sciences, University of Texas Health Science Center, Dallas, TX. Anjali C. Perera, BSN RN, is Staff Nurse, Department of Neurology & Neurotherapeutics, UT Southwestern, Dallas, TX. Arianna Barnes, BSN RN, is Staff Nurse, Intensive Care, Mission Hospital, Providence St. Joseph Health, Mission Viejo, CA. Venkatesh Aiyagari, MD, is Professor, Department of Neurological Surgery, UT Southwestern, Dallas, TX.
Background:
Automated pupillometry is becoming widely accepted as an objective measure of pupillary function, especially in neurocritical care units. Normative reference values and thresholds to denote a significant change are necessary for integrating automated pupillometry into practice.
Objective:
Providing point estimates of normal ranges for pupillometry data will help clinicians intuit meaning from these data that will drive clinical interventions.
Methods:
This study used a planned descriptive analysis using data from a multicenter registry including automated pupillometry assessments in 2140 subjects from 3 US hospitals collected during a 3-year period.
Results:
We provide a comprehensive list of admission pupillometry data. Our data demonstrate significant differences in pupillary values for Neurological Pupil Index, latency, and constriction velocity when stratified by age, sex, or severity of illness defined by the Glasgow Coma Scale score.
Conclusion:
This study provides a greater understanding of expected distributions for automated pupillometry values in a wide range of neurocritical care populations.
