Development of an algorithm to identify preoperative medical consultations using administrative data.
BACKGROUND
Preoperative consultation by internal medicine specialists may help improve the care of patients undergoing major surgery. Population-based administrative data are an efficient approach for studying these consultations at a population-level. However, administrative data in many jurisdictions lack specific codes to identify preoperative medical consultations, as opposed to consultations for nonoperative indications.


OBJECTIVE
To develop an accurate claims-based algorithm for identifying preoperative medical consultations before major elective noncardiac surgery.


METHODS
We conducted a multicenter cross-sectional study in Ontario, Canada. Preoperative medical consultations identified by medical record abstraction were compared with those identified by linked administrative data (physician service claims, hospital discharge abstracts).


METHODS
We randomly selected 606 individuals, aged older than 40 years, who underwent elective intermediate-to-high-risk noncardiac surgery at 8 randomly selected hospitals between April 1, 2002 and March 31, 2004.


RESULTS
Medical record abstraction identified preoperative medical consultations in 317 patients (52%). The optimal claims-based algorithm for identifying these consultations was a physician service claim for a consultation by a cardiologist, general internist, endocrinologist, geriatrician, or nephrologist within 4 months before the index surgical procedure. This algorithm had a sensitivity of 90% (95% confidence interval [CI]: 86-93), specificity of 92% (95% CI: 88-95), positive predictive value of 93% (95% CI: 89-95), and negative predictive value of 90% (95% CI: 86-93).


CONCLUSIONS
A simple claims-based algorithm can accurately identify preoperative medical consultations before major elective noncardiac surgery. This algorithm may help enhance population-based evaluations of preoperative care, provided that the requisite linked administrative healthcare data are present.
Wijeysundera
Duminda N
DN
Institute for Clinical Evaluative Sciences, Toronto, ON, Canada. d.wijeysundera@utoronto.ca
Austin
Peter C
PC
Hux
Janet E
JE
Beattie
W Scott
WS
Buckley
D Norman
DN
Laupacis
Andreas
A
D000328:Adult; D000465:Algorithms; D003430:Cross-Sectional Studies; D017558:Elective Surgical Procedures; D005260:Female; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data*; D008297:Male; D057234:Preoperative Period; D012017:Referral and Consultation / Q000458:organization & administration*; D015203:Reproducibility of Results