Comparison of two computer algorithms to identify surgical site infections.
Background: Surgical site infections (SSIs), the second most common healthcare-associated infections, increase hospital stay and healthcare costs significantly. Traditional surveillance of SSIs is labor-intensive. Mandatory reporting and new non-payment policies for some SSIs increase the need for e...
| Publicado en: | Surgical Infections Vol. 12; no. 6; pp. 459 - 465 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Mary Ann Liebert, Inc.
2011 Dec
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=108214053&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 108214053 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10962964 N3P jtl: Surgical Infections issn: 10962964 maglogo: N pubinfo: dt: 2011 Dec vid: 12 iid: 6 pid: 1365 pub: Mary Ann Liebert, Inc. place: New Rochelle, New York artinfo: ui: 108214053 108214053 NLM22136489 2011406782 10.1089/sur.2010.109 NLM22136489 PMC3279712 108214053 ppf: 459 ppct: 6 formats: tig: atl: Comparison of two computer algorithms to identify surgical site infections. aug: au: Apte M Landers T Furuya Y Hyman S Larson E Apte, Mandar Landers, Timothy Furuya, Yoko Hyman, Sandra Larson, Elaine affil: Center for Interdisciplinary Research on Antibiotic Resistance, School of Nursing, Columbia University, New York, New York, USA sug: subj: Algorithms Data Collection Methods Diagnosis, Computer Assisted Surgical Wound Infection Epidemiology International Classification of Diseases Length of Stay Statistics and Numerical Data New York Readmission Statistics and Numerical Data Reoperation Statistics and Numerical Data ab: Background: Surgical site infections (SSIs), the second most common healthcare-associated infections, increase hospital stay and healthcare costs significantly. Traditional surveillance of SSIs is labor-intensive. Mandatory reporting and new non-payment policies for some SSIs increase the need for efficient and standardized surveillance methods. Computer algorithms using administrative, clinical, and laboratory data collected routinely have shown promise for complementing traditional surveillance.Methods: Two computer algorithms were created to identify SSIs in inpatient admissions to an urban, academic tertiary-care hospital in 2007 using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes (Rule A) and laboratory culture data (Rule B). We calculated the number of SSIs identified by each rule and both rules combined and the percent agreement between the rules. In a subset analysis, the results of the rules were compared with those of traditional surveillance in patients who had undergone coronary artery bypass graft surgery (CABG).Results: Of the 28,956 index hospital admissions, 5,918 patients (20.4%) had at least one major surgical procedure. Among those and readmissions within 30 days, the ICD-9-CM-only rule identified 235 SSIs, the culture-only rule identified 287 SSIs; combined, the rules identified 426 SSIs, of which 96 were identified by both rules. Positive and negative agreement between the rules was 36.8% and 97.1%, respectively, with a kappa of 0.34 (95% confidence interval [CI] 0.27-0.41). In the subset analysis of patients who underwent CABG, of the 22 SSIs identified by traditional surveillance, Rule A identified 19 (86.4%) and Rule B identified 13 (59.1%) cases. Positive and negative agreement between Rules A and B within these "positive controls" was 81.3% and 50.0% with a kappa of 0.37 (95% CI 0.04-0.70).Conclusion: Differences in the rates of SSI identified by computer algorithms depend on sources and inherent biases in electronic data. Different algorithms may be appropriate, depending on the purpose of case identification. Further research on the reliability and validity of these algorithms and the impact of changes in reimbursement on clinician practices and electronic reporting is suggested. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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