The Effect of Adding Comorbidities to Current Centers for Disease Control and Prevention Central-Line-Associated
Sarah S Jackson1, Surbhi Leekha1, Laurence S Magder1
11Department of Epidemiology and Public Health,University of Maryland School of Medicine,Baltimore,Maryland.
Insights
Improving central-line-associated bloodstream infection (CLABSI) risk adjustment is crucial for fair hospital comparisons. Incorporating patient comorbidities significantly enhances the accuracy of CLABSI rate predictions.
Area of Science:
- Healthcare Quality Improvement
- Infectious Disease Epidemiology
- Health Services Research
Background:
- Accurate risk adjustment is essential for comparing hospital-acquired infection rates, specifically central-line-associated bloodstream infections (CLABSI).
- Current Centers for Disease Control and Prevention (CDC) methodologies for CLABSI risk adjustment have limitations, primarily focusing on intensive care unit (ICU) type, hospital size, and medical school affiliation.
Purpose of the Study:
- To evaluate the effectiveness of incorporating patient demographics and comorbidities, extracted from electronic hospital discharge codes, into risk adjustment models for CLABSI rates.
- To compare the discriminatory power of a new risk-adjustment model against the existing CDC methodology.
Main Methods:
- A cohort study analyzed data from 85,849 ICU patients across 22 hospitals between January 2012 and December 2013.
- CLABSIs were identified by infection preventionists, and patient data, including International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) codes, were collected.
- Two models were compared: one adjusting for ICU type alone, and another incorporating ICU type plus patient case-mix (demographics and comorbidities). Model performance was assessed using C statistics and changes in hospital rankings based on standardized infection ratios (SIRs).
Main Results:
- The inclusion of patient comorbidities (coagulopathy, paralysis, renal failure, malnutrition, and age) significantly improved the risk-adjustment model's predictive ability (C statistic increased from 0.55 to 0.64).
- Hospital rankings based on SIRs changed for 45% of hospitals when comorbidity data was added, indicating a substantial impact on comparative performance.
- The overall CLABSI rate in the study cohort was 0.2% (162 cases).
Conclusions:
- Risk-adjustment models for CLABSI that utilize electronically available comorbidity data demonstrate superior discrimination compared to the current CDC model.
- The findings strongly suggest that the CDC should consider integrating comorbidity-based risk adjustment to achieve more accurate and equitable comparisons of CLABSI rates across healthcare facilities.
Abstract:
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.
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