OBJECTIVE: To develop prognostic models for improved risk adjustment in surgical site infection surveillance for 5 surgical procedures and to compare these models with the National Nosocomial Infection Surveillance system (NNIS) risk index. DESIGN: In a multicenter cohort study, prospective assessment of surgical site infection and risk factors was performed from 1996 to 2000. In addition, risk factors abstracted from patient files, available in a national medical register, were used. The c-index was used to measure the ability of procedure-specific logistic regression models to predict surgical site infection and to compare these models with models based on the NNIS risk index. A c-index of 0.5 indicates no predictive power, and 1.0 indicates perfect predictive power. SETTING: Sixty-two acute care hospitals in the Dutch national surveillance network for nosocomial infections. PARTICIPANTS: Patients who underwent 1 of 5 procedures for which the predictive ability of the NNIS risk index was moderate: reconstruction of the aorta (n=875), femoropopliteal or femorotibial bypass (n=641), colectomy (n=1,142), primary total hip prosthesis (n=13,770), and cesarean section (n=2,962). RESULTS: The predictive power of the new model versus the NNIS index was 0.75 versus 0.62 for reconstruction of the aorta (P<.01), 0.78 versus 0.58 for femoropopliteal or femorotibial bypass (P<.001), 0.69 versus 0.62 for colectomy (P<.001), 0.64 versus 0.56 for primary total hip prosthesis arthroplasty (P<.001), and 0.70 versus 0.54 for cesarean section (P<.001). CONCLUSION: Data available from hospital information systems can be used to develop models that are better at predicting the risk of surgical site infection than the NNIS risk index. Additional data collection may be indicated for certain procedures--for example, total hip prosthesis arthroplasty.
OBJECTIVE: To develop prognostic models for improved risk adjustment in surgical site infection surveillance for 5 surgical procedures and to compare these models with the National Nosocomial Infection Surveillance system (NNIS) risk index. DESIGN: In a multicenter cohort study, prospective assessment of surgical site infection and risk factors was performed from 1996 to 2000. In addition, risk factors abstracted from patient files, available in a national medical register, were used. The c-index was used to measure the ability of procedure-specific logistic regression models to predict surgical site infection and to compare these models with models based on the NNIS risk index. A c-index of 0.5 indicates no predictive power, and 1.0 indicates perfect predictive power. SETTING: Sixty-two acute care hospitals in the Dutch national surveillance network for nosocomial infections. PARTICIPANTS: Patients who underwent 1 of 5 procedures for which the predictive ability of the NNIS risk index was moderate: reconstruction of the aorta (n=875), femoropopliteal or femorotibial bypass (n=641), colectomy (n=1,142), primary total hip prosthesis (n=13,770), and cesarean section (n=2,962). RESULTS: The predictive power of the new model versus the NNIS index was 0.75 versus 0.62 for reconstruction of the aorta (P<.01), 0.78 versus 0.58 for femoropopliteal or femorotibial bypass (P<.001), 0.69 versus 0.62 for colectomy (P<.001), 0.64 versus 0.56 for primary total hip prosthesis arthroplasty (P<.001), and 0.70 versus 0.54 for cesarean section (P<.001). CONCLUSION: Data available from hospital information systems can be used to develop models that are better at predicting the risk of surgical site infection than the NNIS risk index. Additional data collection may be indicated for certain procedures--for example, total hip prosthesis arthroplasty.
Authors: Robert R Cima; John R Bergquist; Kristine T Hanson; Cornelius A Thiels; Elizabeth B Habermann Journal: J Gastrointest Surg Date: 2017-05-03 Impact factor: 3.452
Authors: Margaret A Olsen; James Higham-Kessler; Deborah S Yokoe; Anne M Butler; Johanna Vostok; Kurt B Stevenson; Yosef Khan; Victoria J Fraser Journal: Infect Control Hosp Epidemiol Date: 2009-11 Impact factor: 3.254
Authors: Margaret A Olsen; Mellani Lefta; Jill R Dietz; Keith E Brandt; Rebecca Aft; Ryan Matthews; Jennie Mayfield; Victoria J Fraser Journal: J Am Coll Surg Date: 2008-06-26 Impact factor: 6.113
Authors: Håvard Dale; Inge Skråmm; Hege L Løwer; Hanne M Eriksen; Birgitte Espehaug; Ove Furnes; Finn Egil Skjeldestad; Leif I Havelin; Lars B Engesaeter Journal: Acta Orthop Date: 2011-11-09 Impact factor: 3.717