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Experts classify comorbid conditions as risk factors for CLABSI and SSI
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Researchers surveyed a panel of experts to classify comorbid conditions as risk factors for central-line associated bloodstream infection and surgical-site infection.
According to Anthony D. Harris, MD, MPH, professor at the University of Maryland School of Medicine, and colleagues, an expert consensus was reached on 14 comorbid conditions being causally related to central-line associated bloodstream infections (CLABSIs) and 17 being causally related to surgical-site infections (SSIs).
The researchers identified comorbid conditions based on International Classification of Diseases (ICD)-based risk factor studies and future risk-adjustment studies.
“Our results have produced a list of comorbid conditions that should be analyzed as risk factors for, and further explored for risk adjustment of, CLABSI and SSI,” Harris and colleagues wrote in Infection Control and Hospital Epidemiology.
Two rounds of surveys
Harris and colleagues used the Delphi method to form a consensus among experts as to which comorbid conditions are related or unrelated to CLABSIs and SSIs.
In a two-round survey, they asked nine infectious disease and infection control experts to rate 35 comorbid conditions on a scale of 1 to 5 based on their perceived causal relatedness to CLABSI and SSI outcomes, with 1 meaning “not at all related” and 5 equaling “strongly related.” The researchers selected the experts based on qualifications that included authorship of studies or guidelines related to CLABSI and SSI, leadership of institutional health care epidemiology programs, and prominence in national infectious disease and health care epidemiology societies.
After the first survey, each respondent received a summary showing their rating and the rating of the other experts for each condition. The experts were then allowed to modify their ratings following a 2-hour conference call discussion moderated by the principle investigator.
Harris and colleagues considered an expert consensus to have been reached on the causal relatedness of a condition to CLABSI and SSI if three things were true: more than 50% of the experts rated the condition as a 3 (“somewhat related”) or higher; and the interquartile range (IQR) and standard deviation (SD) were both less than or equal to 1.
‘Improvement in consensus’
After the second survey, 40% of comorbid conditions (n = 14) were regarded by the experts as being causally related to CLABSIs and 49% (n = 17) were perceived to be causally related to SSIs, according to Harris and colleagues.
In the first survey, no condition received 100% consensus from the experts as having a causal relatedness to CLABSIs. However, after the conference call, lymphoma, malignancy, metastatic solid tumor and obesity received ratings of 3 or higher from all nine experts in the second round of surveying.
For SSIs, 14 conditions received a rating of 3 or higher in the second round of surveying from all nine experts, including five — diabetes, diabetes with complication, lymphoma, metastatic solid tumor, and obesity — for which a 100% consensus had already been reached after the first round.
Conversely, comorbid conditions were considered causally unrelated to CLABSIs and SSIs if most experts rated them a 1 and the IQR and SD were both less than or equal to 1. This was true for 29% of the conditions for CLABSI and 23% for SSI.
For CLABSIs, the IQR and SD decreased for ratings of 60% (n = 21) and 97% (n = 34) of comorbid conditions, respectively, and for 49% (n = 17) and 91% (n = 32) of comorbid conditions for SSIs, “suggesting improvement in consensus among this group of experts,” Harris and colleagues concluded.
“Large studies involving multiple hospitals exploring ICD-based risk-factor analysis and risk-factor adjustment are needed,” they wrote. “We hope that the CDC strongly considers including ICD-based comorbid condition risk adjustment in the near future.” – by Gerard Gallagher
Disclosure: The researchers report no relevant financial disclosures.
Perspective
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CLABSIs and SSIs are among the most common health care-associated infections. Both infections are among the metrics upon which hospital quality is publicly reported and to which hospital reimbursement is tied. Determining how best to compare hospital performance fairly among facilities of different types who serve varied patient populations remains a challenge. Current comparisons only take into account a small number of factors — mostly facility-specific — and do not take into account patient factors likely to have an impact on infection risk such as immunosuppression and obesity. The article by Harris and colleagues presents a novel way to use subject matter experts to consider additional patient-level factors that might be important. The capability for this type of risk modeling will only grow as electronic health records improve. These risk factors, if found to be significant, can be used to target interventions to higher-risk patients and to allow a fairer comparison between facilities.
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