Vital sign measurements demonstrate terminal digit bias and boundary effects

Author:

Kleinig Oliver12ORCID,To Minh‐Son3,Ovenden Christopher D.12,Kovoor Joshua G.1245ORCID,Goh Rudy26,Lam Lydia12,Wenzel Tara3,Tan Yiran12,Harish Hrishikesh12,Gupta Aashray K.24,Gluck Sam16,Gilbert Toby12,Bacchi Stephen123ORCID

Affiliation:

1. Department of Neurology, Department of Neurosurgery Royal Adelaide Hospital Adelaide South Australia Australia

2. School of Medicine University of Adelaide Adelaide South Australia Australia

3. Department of Radiology, Department of Neurology Flinders University Adelaide South Australia Australia

4. Department of Cardiothoracic Surgery Gold Coast University Hospital Gold Coast South Australia Australia

5. Department of Surgery Ballarat Base Hospital Ballarat Victoria Australia

6. Department of Neurology Lyell McEwin Hospital Adelaide South Australia Australia

Abstract

AbstractObjectiveThe measurement and recording of vital signs may be impacted by biases, including preferences for even and round numbers. However, other biases, such as variation due to defined numerical boundaries (also known as boundary effects), may be present in vital signs data and have not yet been investigated in a medical setting. We aimed to assess vital signs data for such biases. These parameters are clinically significant as they influence care escalation.MethodsVital signs data (heart rate, respiratory rate, oxygen saturation and systolic blood pressure) were collected from a tertiary hospital electronic medical record over a 2‐year period. These data were analysed using polynomial regression with additional terms to assess for underreporting of out‐of‐range observations and overreporting numbers with terminal digits of 0 (round numbers), 2 (even numbers) and 5.ResultsIt was found that heart rate, oxygen saturation and systolic blood pressure demonstrated ‘boundary effects’, with values inside the ‘normal’ range disproportionately more likely to be recorded. Even number bias was observed in systolic heart rate, respiratory rate and blood pressure. Preference for multiples of 5 was observed for heart rate and blood pressure. Independent overrepresentation of multiples of 10 was demonstrated in heart rate data.ConclusionAlthough often considered objective, vital signs data are affected by bias. These biases may impact the care patients receive. Additionally, it may have implications for creating and training machine learning models that utilise vital signs data.

Publisher

Wiley

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