Improving public reporting and data validation for complex surgical site infections after coronary artery bypass graft surgery and hip arthroplasty.
Improving public reporting and data validation for complex surgical site infections after coronary artery bypass graft surgery and hip arthroplasty.
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DOI:
10.1093/ofid/ofu106
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发表时间:
2014-12
影响因子:
4.2
通讯作者:
Huang SS
中科院分区:
文献类型:
--
作者:
Calderwood MS;Kleinman K;Murphy MV;Platt R;Huang SS
Diagnosis codes in claims submitted for reimbursement following coronary artery bypass graft surgery and hip arthroplasty allow standardized and efficient identification of deep and organ/space surgical site infections. Deep and organ/space surgical site infections (D/OS SSI) cause significant morbidity, mortality, and costs. Rates are publicly reported and increasingly used as quality metrics affecting hospital payment. Lack of standardized surveillance methods threaten the accuracy of reported data and decrease confidence in comparisons based upon these data. We analyzed data from national validation studies that used Medicare claims to trigger chart review for SSI confirmation after coronary artery bypass graft surgery (CABG) and hip arthroplasty. We evaluated code performance (sensitivity and positive predictive value) to select diagnosis codes that best identified D/OS SSI. Codes were analyzed individually and in combination. Analysis included 143 patients with D/OS SSI after CABG and 175 patients with D/OS SSI after hip arthroplasty. For CABG, 9 International Classification of Diseases, 9th Revision (ICD-9) diagnosis codes identified 92% of D/OS SSI, with 1 D/OS SSI identified for every 4 cases with a diagnosis code. For hip arthroplasty, 6 ICD-9 diagnosis codes identified 99% of D/OS SSI, with 1 D/OS SSI identified for every 2 cases with a diagnosis code. This standardized and efficient approach for identifying D/OS SSI can be used by hospitals to improve case detection and public reporting. This method can also be used to identify potential D/OS SSI cases for review during hospital audits for data validation.