Which Comorbid Conditions Should We Be Analyzing as Risk Factors for Healthcare-Associated Infections?

Microbiological trends and mortality risk factors of central line-associated bloodstream infections in an academic medical center 2015-2020

BACKGROUND

Despite tremendous efforts to prevent central line-associated bloodstream infections, they still remain life-threatening complications among hospitalized patients with significant morbidity and mortality worldwide. The emerging antibiotic-resistant bacteria and other risk factors, including patient comorbidities, complicate patient management.

METHODS

A single-center retrospective observational study was conducted at King Fahad Hospital of the University, Eastern Province, Saudi Arabia. Hospitalized patients with confirmed central line-associated bloodstream infections between January 2015 and December 2020 were included. The primary objectives were to investigate the trends in antibiotic susceptibility patterns of the causative agents, coexisting comorbid conditions, and other risk factors associated with mortality.

RESULTS

A total of 214 patients with confirmed central line-associated bloodstream infections were included (CLABSI). The overall 30-day mortality rate was 33.6%. The infection rates per 1000 central line days for medical, surgical, and pediatric intensive care units were 4.97, 2.99, and 4.56 per 1000 CL days, respectively. The overall microbiological trends showed a predominance of Gram-negative agents, a steady increase of fungal CLABSI up to 24.0% in 2020, and a high prevalence of multidrug resistance up to 47% of bacterial CLABSI. In addition, the study indicates a significant negative surviving correlation with diabetes mellitus, cardiovascular disease, lung disease, chronic kidney disease, and the presence of ≥ 3 comorbidities (P < 0.05).

CONCLUSION

The microbiological trends of the study population demonstrated a steady increase of CLABSI caused by Candida spp. with a predominance of Gram-negative pathogens. Stratifying the patients according to relevant mortality risk factors, including patient comorbidities, will help reduce CLABSI rates and improve patient outcomes.

Electronically Available Patient Claims Data Improve Models for Comparing Antibiotic Use Across Hospitals: Results from 576 U.S. Facilities

Background

The Centers for Disease Control and Prevention (CDC) uses standardized antimicrobial administration ratios (SAARs)—that is, observed-to-predicted ratios—to compare antibiotic use across facilities. CDC models adjust for facility characteristics when predicting antibiotic use but do not include patient diagnoses and comorbidities that may also affect utilization. This study aimed to identify comorbidities causally related to appropriate antibiotic use and to compare models that include these comorbidities and other patient-level claims variables to a facility model for risk-adjusting inpatient antibiotic utilization.

Methods

The study included adults discharged from Premier Database hospitals in 2016–2017. For each admission, we extracted facility, claims, and antibiotic data. We evaluated 7 models to predict an admission’s antibiotic days of therapy (DOTs): a CDC facility model, models that added patient clinical constructs in varying layers of complexity, and an external validation of a published patient-variable model. We calculated hospital-specific SAARs to quantify effects on hospital rankings. Separately, we used Delphi Consensus methodology to identify Elixhauser comorbidities associated with appropriate antibiotic use.

Results

The study included 11 701 326 admissions across 576 hospitals. Compared to a CDC-facility model, a model that added Delphi-selected comorbidities and a bacterial infection indicator was more accurate for all antibiotic outcomes. For total antibiotic use, it was 24% more accurate (respective mean absolute errors: 3.11 vs 2.35 DOTs), resulting in 31–33% more hospitals moving into bottom or top usage quartiles postadjustment.

Conclusions

Adding electronically available patient claims data to facility models consistently improved antibiotic utilization predictions and yielded substantial movement in hospitals’ utilization rankings.

Electronically Available Comorbidities Should Be Used in Surgical Site Infection Risk Adjustment

Background

Healthcare-associated infections such as surgical site infections (SSIs) are used by the Centers for Medicare and Medicaid Services (CMS) as pay-for-performance metrics. Risk adjustment allows a fairer comparison of SSI rates across hospitals. Until 2016, Centers for Disease Control and Prevention (CDC) risk adjustment models for pay-for-performance SSI did not adjust for patient comorbidities. New 2016 CDC models only adjust for body mass index and diabetes.

Methods

We performed a multicenter retrospective cohort study of patients undergoing surgical procedures at 28 US hospitals. Demographic data and International Classification of Diseases, Ninth Revision codes were obtained on patients undergoing colectomy, hysterectomy, and knee and hip replacement procedures. Complex SSIs were identified by infection preventionists at each hospital using CDC criteria. Model performance was evaluated using measures of discrimination and calibration. Hospitals were ranked by SSI proportion and risk-adjusted standardized infection ratios (SIR) to assess the impact of comorbidity adjustment on public reporting.

Results

Of 45394 patients at 28 hospitals, 573 (1.3%) developed a complex SSI. A model containing procedure type, age, race, smoking, diabetes, liver disease, obesity, renal failure, and malnutrition showed good discrimination (C-statistic, 0.73) and calibration. When comparing hospital rankings by crude proportion to risk-adjusted ranks, 24 of 28 (86%) hospitals changed ranks, 16 (57%) changed by ≥2 ranks, and 4 (14%) changed by >10 ranks.

Conclusions

We developed a well-performing risk adjustment model for SSI using electronically available comorbidities. Comorbidity-based risk adjustment should be strongly considered by the CDC and CMS to adequately compare SSI rates across hospitals.

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.

Antibiotic Trends Amid Multidrug-Resistant Gram-Negative Infections in Intensive Care Units

Isolates from ICUs most commonly find multidrug-resistant (MDR) gram-negative bacteria. The purpose of this article is to discuss the significant impact MDR gram-negative infections are having on ICUs, the threat on health and mortality, and effective and new approaches aimed to combat MDR gram-negative infections in critically ill populations. Inappropriate antibiotic therapies for suspected or documented infections are the leading cause of the emergence of bacterial resistance. A variety of strategies are aimed at combatting this international burden via antibiotic stewardship programs. Studies are demonstrating promise against the virulence MDR gram-negative infections have posed.