Abstract
Background
Thirty-day complications frequently serve in the surgical literature as a quality
indicator. This metric is not meant to capture the full array of complication resulting
from surgical intervention. However, this period is largely based on convention, with
little evidence to support it. This study sought to determine the optimal surveillance
period for postsurgical complications, defined as the shortest period that also encompassed
the highest proportion of postsurgical adverse events.
Methods
TRICARE data (2006-2014) were queried for adult (18-64 y) patients who underwent one
of 11 surgical procedures. Patients were assessed for complications up to 90 d after
surgery. Kaplan–Meier curves, linear spline regression models at each incremental
postsurgical day, and adjusted R-squared values were used to identify critical time
point cutoffs for the surveillance of complications. Optimal length of surveillance
was defined as the postsurgical day on which the model demonstrated the highest R-squared
value. A supplemental analysis considered these measures for orthopedic and general
surgical procedures.
Results
One lakh ninety-eight patients met the inclusion criteria. A total of 21.8% patients
experienced at least one complication during the follow-up period, with 59% occurring
within the first 15 d. Kaplan–Meier curves for complications showed a demonstrable
inflection before 20 d and 14-15 d possessed the highest R-squared values.
Conclusions
In this analysis, the optimal surveillance period for postsurgical complications was
15 d. While the conventional 30-d period may still be appropriate for a variety of
reasons, the shorter interval identified here may represent a superior quality measure
specific to surgical practice.
Keywords
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Article info
Publication history
Published online: July 17, 2018
Accepted:
June 19,
2018
Received in revised form:
June 8,
2018
Received:
March 5,
2018
Footnotes
Presentation: Presented as an oral presentation at the Academic Surgical Congress, Jacksonville, FL, 2018.
Identification
Copyright
© 2018 Elsevier Inc. All rights reserved.
