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Development of an Algorithm to Identify Preoperative Medical Consultations Using Administrative Data

Dados Bibliográficos

ID9102260
AutoresDuminda N Wijeysundera (0000-0002-5897-8605, Institute for Clinical Evaluative Sciences, autor correspondente), Peter C Austin (0000-0003-3337-233X, St. Michael's Hospital, autor correspondente), Janet E Hux (St. Michael's Hospital, autor correspondente), W Scott Beattie (0000-0002-9870-0405, St. Michael's Hospital), D Norman Buckley (0000-0003-1150-0572, McMaster University), Andreas Laupacis (0000-0003-1380-3032, autor correspondente)
Ano2009
Volume47
Fascículo12
Páginas1258-1264
Data de publicação2009-12-01
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoMedical Care (JOURNAL)
Identificadores do periódicoISSN: 0025-7079 • E-ISSN: 1537-1948
EditoraOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0b013e3181bd479c
PMID19890221
OpenAlexW2089460471
IdiomaEN
Referências citadas20

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. RESEARCH DESIGN: 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). SUBJECTS: 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

Confidence interval · Elective surgery · Family medicine · Medical record · MEDLINE · Population · Preoperative care · Cardiac, Anesthesia and Surgical Outcomes · Emergency Medicine · Enhanced Recovery After Surgery · Internal Medicine · Medicine · Music Therapy and Health · Surgery

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