Abstract
Background
Vital signs are routinely measured from patients presenting to the emergency department
(ED), but how they predict clinical outcomes like hospitalization is unclear.
Objectives
To evaluate how pulse, respiratory rate, temperature, and mean arterial pressure (MAP)
at ED presentation predicted probability of hospitalization, transfer to another center,
or death in the ED (as a composite outcome) vs. other ED dispositions (discharged,
eloped, and sent to observation or labor and delivery), and to assess the performance
of different modeling strategies, specifically, models including flexible forms of
vital signs (as restricted cubic splines) vs. linear forms (untransformed numeric
variables) vs. binary transformations (vital signs values categorized simply as normal
or abnormal).
Methods
We analyzed the data of 12,660 adults presenting for medical illnesses to the ED at
the University of California, San Francisco Medical Center, San Francisco, California,
throughout 2014. We used flexible forms of vital signs data at presentation (pulse,
temperature, respiratory rate, and MAP) to predict ED disposition (admitted, transferred,
or died, vs. other ED dispositions) and to guide binary transformation of vital signs.
We compared performance of models including vital signs as flexible terms, binary
transformations, or linear terms.
Results
A model including flexible forms of vital signs and age to predict the outcome had
good calibration and moderate discrimination (C-statistic = 71.2, 95% confidence interval
[CI] 70.0–72.4). Binary transformation of vital signs had minimal impact on performance
(C-statistic = 71.3, 95% CI 70.2–72.5). A model with linear forms was less calibrated
and had slightly reduced discrimination (C-statistic = 70.3, 95% CI 69.1–71.5).
Conclusions
Findings suggest that flexible modeling of vital signs may better reflect their association
with clinical outcomes. Future studies to evaluate how vital signs could assist clinical
decision-making in acute care settings are suggested.
Keywords
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Article info
Publication history
Published online: January 13, 2020
Accepted:
November 10,
2019
Received in revised form:
October 28,
2019
Received:
April 27,
2019
Identification
Copyright
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