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Special Issue

Automated Methods for Surveillance of Surgical Site Infections

Richard Platt,*† Deborah S. Yokoe,† Kenneth E. Sands,‡ and the CDC Eastern Massachusetts Prevention Epicenter Investigators (1)
*Harvard Medical School and Harvard Pilgrim Health Care, Boston, Massachusetts, USA; Harvard Medical School and Brigham and Women's Hospital, Boston, Massachusetts, USA; and Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA

Figure. Performance of various methods for detection of postdischarge surgical site infections for 4,086 nonobstetric surgical procedures with no inpatient infection. Lines represent fitted receiver operating characteristic (ROC) curves for three logistic regression models, which differ by data sources available for generating probabilities. Points represent performance of four different recursive partitioning models and data from patient and physician surveys. For analyses limited to hospital data and outpatient antibiotic (Abx) dispensing data, the logistic regression model had equivalent performance to classification trees at the points shown. The fitted ROC curve falls below this point because most procedures clustered around a few discrete probabilities and limited data points cause approximation of the ROC curve to be less accurate. The recursive partitioning high-cost model accepts 15 false-positives at the margin to capture one true infection; the low-cost model accepts 5 false positives at the margin (24).

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Figure. Performance of various methods for detection of postdischarge surgical site infections for 4,086 nonobstetric surgical procedures with no inpatient infection. Lines represent fitted receiver operating characteristic (ROC) curves for three logistic regression models, which differ by data sources available for generating probabilities. Points represent performance of four different recursive partitioning models and data from patient and physician surveys. For analyses limited to hospital data and outpatient antibiotic (Abx) dispensing data, the logistic regression model had equivalent performance to classification trees at the points shown. The fitted ROC curve falls below this point because most procedures clustered around a few discrete probabilities and limited data points cause approximation of the ROC curve to be less accurate. The recursive partitioning high-cost model accepts 15 false-positives at the margin to capture one true infection; the low-cost model accepts 5 false positives at the margin (24). (Figure originally published in Sands et al. Journal of Infectious Diseases 1999;179:434. Copyright 1999, University of Chicago Press. Reprinted with permission.)
 


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