How infection present at time of surgery (PATOS) data impacts your surgical site infection (SSI) standardized infection ratios (SIR), with focus on the complex 30-day SSI SIR model
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

For very narrow results

When looking for a specific result

Best used for discovery & interchangable words

Recommended to be used in conjunction with other fields

Dates

to

Document Data
Library
People
Clear All
Clear All

For additional assistance using the Custom Query please check out our Help Page

i

How infection present at time of surgery (PATOS) data impacts your surgical site infection (SSI) standardized infection ratios (SIR), with focus on the complex 30-day SSI SIR model

Filetype[PDF-309.04 KB]


English

Details:

  • Alternative Title:
    Am J Infect Control
  • Personal Author:
  • Description:
    This case study is part of a series centered on the Centers for Disease Control and Prevention's National Healthcare Safety Network's (NHSN) health care-associated infection (HAI) surveillance definitions. This is the first analytic case study published in AJIC since the CDC/ NHSN updated its HAI risk adjustment models and rebaselined the standardized infection ratios (SIRs) in 2015. This case describes a scenario that Infection Preventionists (IPs) have encountered during their analysis of surgical site infection (SSI) surveillance data. The case study is intended to illustrate how specific models can impact the SIR results by highlighting differences in the criteria for NHSN's older and newer risk models: the original versions and the updated models introduced in 2015. Understanding these differences provides insight into how SSI SIR calculations differ between the older and newer NHSN baseline models. NHSN plans to produce another set of HAI risk adjustment models in the future, using newer HAI incidence and risk factor data. While the timetable for these changes remains to be determined, the statistical methods used to produce future models and SIR calculations will continue the precedents that NHSN has established. An online survey link is provided where participants may confidentially answer questions related to the case study and receive immediate feedback in the form of correct answers, explanations, rationales, and summary of teaching points. Details of the case study, answers, and explanations have been reviewed and approved by NHSN staff. We hope that participants take advantage of this educational offering and thereby gain a greater understanding of the NHSN's HAI data analysis. There are 2 baselines available for SSI standardized infection ration (SIRs) in the National Healthcare Safety Network (NHSN); one based on the 2006-2008 national aggregate data and another based on the 2015 data. Each of the 2 baselines has a different set of inclusion criteria for the SSI data, which impact the calculation of the SIR. In this case study, we focused on the impact of the inclusion of PATOS in the calculation of the 2006-2008 baseline SSI SIR and the exclusion of PATOS from the calculation of the 2015 baseline SSI SIR. In the 2006-2008 baseline SSI SIRs, PATOS events and the procedures to which they are linked are included in the calculation of the SSI SIR whereas in the 2015 baseline SSI SIRs, PATOS events and the procedures to which they are linked are excluded from the calculation of the SSI SIR. Meaning, if we control for all other inclusion criteria other than PATOS data for both baselines, we will notice differences in the number of observed events as well as the number of predicted infections for the 2 baselines. For details of the 2015 baseline and risk adjustment calculation, please review the NHSN Guide to the SIR referenced below. For details of the 2006-2008 baseline4 and risk adjustment, please see the SHEA paper "Improving Risk-Adjusted Measures of Surgical Site Infection for the National Healthcare Safety Network" by author Yi Mu.
  • Subjects:
  • Source:
  • Pubmed ID:
    34689884
  • Pubmed Central ID:
    PMC9641641
  • Document Type:
  • Funding:
  • Volume:
    49
  • Issue:
    11
  • Collection(s):
  • Main Document Checksum:
  • Download URL:
  • File Type:

Supporting Files

More +

You May Also Like

Checkout today's featured content at stacks.cdc.gov