Denominator doesn't matter: standardizing healthcare-associated infection rates by bed days or device days

Random variation drives a critical bias in the comparison of healthcare-associated infections

OBJECTIVE

To evaluate random effects of volume (patient days or device days) on healthcare-associated infections (HAIs) and the standardized infection ratio (SIR) used to compare hospitals.

DESIGN

A longitudinal comparison between publicly reported quarterly data (2014-2020) and volume-based random sampling using 4 HAI types: central-line-associated bloodstream infections, catheter-associated urinary tract infections, Clostridioides difficile infections, methicillin-resistant Staphylococcus aureus infections.

METHODS

Using 4,268 hospitals with reported SIRs, we examined relationships of SIRs to volume and compared distributions of SIRs and numbers of reported HAIs to the outcomes of simulated random sampling. We included random expectations into SIR calculations to produce a standardized infection score (SIS).

RESULTS

Among hospitals with volumes less than the median, 20%-33% had SIRs of 0, compared to 0.3%-5% for hospitals with volumes higher than the median. Distributions of SIRs were 86%-92% similar to those based on random sampling. Random expectations explained 54%-84% of variation in numbers of HAIs. The use of SIRs led hundreds of hospitals with more infections than either expected at random or predicted by risk-adjusted models to rank better than other hospitals. The SIS mitigated this effect and allowed hospitals of disparate volumes to achieve better scores while decreasing the number of hospitals tied for the best score.

CONCLUSIONS

SIRs and numbers of HAIs are strongly influenced by random effects of volume. Mitigating these effects drastically alters rankings for HAI types and may further alter penalty assignments in programs that aim to reduce HAIs and improve quality of care.

Urinary Tract Infections

Catheter-associated urinary tract infection (CAUTI) remains one of the most prevalent, yet preventable, health care-associated infections and predominantly occurs in patients with indwelling urinary catheters. Targeted strategies for prevention of CAUTI include limiting urinary catheter use; physician reminder systems, nurse-initiated discontinuation protocols, and automatic stop orders have successfully decreased catheter duration. Alternatives to indwelling catheters should be considered in appropriate patients. If indwelling catheterization is necessary, proper aseptic practices for catheter insertion and maintenance and closed catheter collection system is essential for preventing CAUTI. The use of "bladder bundles" and collaboratives aids in the effective implementation of CAUTI prevention measures.

The Effect of Adding Comorbidities to Current Centers for Disease Control and Prevention Central-Line-Associated Bloodstream Infection Risk-Adjustment Methodology

BACKGROUND Risk adjustment is needed to fairly compare central-line-associated bloodstream infection (CLABSI) rates between hospitals. Until 2017, the Centers for Disease Control and Prevention (CDC) methodology adjusted CLABSI rates only by type of intensive care unit (ICU). The 2017 CDC models also adjust for hospital size and medical school affiliation. We hypothesized that risk adjustment would be improved by including patient demographics and comorbidities from electronically available hospital discharge codes. METHODS Using a cohort design across 22 hospitals, we analyzed data from ICU patients admitted between January 2012 and December 2013. Demographics and International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) discharge codes were obtained for each patient, and CLABSIs were identified by trained infection preventionists. Models adjusting only for ICU type and for ICU type plus patient case mix were built and compared using discrimination and standardized infection ratio (SIR). Hospitals were ranked by SIR for each model to examine and compare the changes in rank. RESULTS Overall, 85,849 ICU patients were analyzed and 162 (0.2%) developed CLABSI. The significant variables added to the ICU model were coagulopathy, paralysis, renal failure, malnutrition, and age. The C statistics were 0.55 (95% CI, 0.51-0.59) for the ICU-type model and 0.64 (95% CI, 0.60-0.69) for the ICU-type plus patient case-mix model. When the hospitals were ranked by adjusted SIRs, 10 hospitals (45%) changed rank when comorbidity was added to the ICU-type model. CONCLUSIONS Our risk-adjustment model for CLABSI using electronically available comorbidities demonstrated better discrimination than did the CDC model. The CDC should strongly consider comorbidity-based risk adjustment to more accurately compare CLABSI rates across hospitals. Infect Control Hosp Epidemiol 2017;38:1019-1024.

A Perspective on the Principles of Integrity in Infectious Disease Research

The medical literature is prone to overstating results, a condition not thoroughly recognized among policymakers. This article sets forth examples of potential problems with research integrity in the infectious disease literature. We describe articles that may be spun, categories lumped together in hopes of creating a significant effect (and sometimes an insignificant one), changes in metrics, and how trials may fail because of suboptimal interventions. When examined together, the examples show that the problems are widespread and illustrate the difficulty associated with interpreting medical research. The state of the current medical literature makes it of utmost importance that all sections of the manuscript are read, including associated letters to the editors and information on ClinicalTrials.gov before authors' recommendations are accepted.

Validation of a Sampling Method to Collect Exposure Data for Central-Line–Associated Bloodstream Infections

OBJECTIVE

Surveillance of central-line–associated bloodstream infections requires the labor-intensive counting of central-line days (CLDs). This workload could be reduced by sampling. Our objective was to evaluate the accuracy of various sampling strategies in the estimation of CLDs in intensive care units (ICUs) and to establish a set of rules to identify optimal sampling strategies depending on ICU characteristics.

DESIGN

Analyses of existing data collected according to the European protocol for patient-based surveillance of ICU-acquired infections in Belgium between 2004 and 2012.

SETTING AND PARTICIPANTS

CLD data were reported by 56 ICUs in 39 hospitals during 364 trimesters.

METHODS

We compared estimated CLD data obtained from weekly and monthly sampling schemes with the observed exhaustive CLD data over the trimester by assessing the CLD percentage error (ie, observed CLDs – estimated CLDs/observed CLDs). We identified predictors of improved accuracy using linear mixed models.

RESULTS

When sampling once per week or 3 times per month, 80% of ICU trimesters had a CLD percentage error within 10%. When sampling twice per week, this was >90% of ICU trimesters. Sampling on Tuesdays provided the best estimations. In the linear mixed model, the observed CLD count was the best predictor for a smaller percentage error. The following sampling strategies provided an estimate within 10% of the actual CLD for 97% of the ICU trimesters with 90% confidence: 3 times per month in an ICU with >650 CLDs per trimester or each Tuesday in an ICU with >480 CLDs per trimester.

CONCLUSION

Sampling of CLDs provides an acceptable alternative to daily collection of CLD data.

Infect Control Hosp Epidemiol 2016;37:549–554

Questionable validity of the catheter-associated urinary tract infection metric used for value-based purchasing

Catheter-associated urinary tract infections (CAUTIs) occur in 290,000 US hospital patients annually, with an estimated cost of $290 million. Two different measurement systems are being used to track the US health care system's performance in lowering the rate of CAUTIs. Since 2010, the Agency for Healthcare Research and Quality (AHRQ) metric has shown a 28.2% decrease in CAUTI, whereas the Centers for Disease Control and Prevention metric has shown a 3%-6% increase in CAUTI since 2009. Differences in data acquisition and the definition of the denominator may explain this discrepancy. The AHRQ metric analyzes chart-audited data and reflects both catheter use and care. The Centers for Disease Control and Prevention metric analyzes self-reported data and primarily reflects catheter care. Because analysis of the AHRQ metric showed a progressive change in performance over time and the scientific literature supports the importance of catheter use in the prevention of CAUTI, it is suggested that risk-adjusted catheter-use data be incorporated into metrics that are used for determining facility performance and for value-based purchasing initiatives.