Improving Risk Adjustment Above Current Centers for Disease Control and Prevention Methodology Using Electronically Available Comorbid Conditions

Healthcare-associated infections and conditions in the era of digital measurement

As the third edition of the Compendium of Strategies to Prevent Healthcare-Associated Infections in Acute Care Hospitals is released with the latest recommendations for the prevention and management of healthcare-associated infections (HAIs), a new approach to reporting HAIs is just beginning to unfold. This next generation of HAI reporting will be fully electronic and based largely on existing data in electronic health record (EHR) systems and other electronic data sources. It will be a significant change in how hospitals report HAIs and how the Centers for Disease Control and Prevention (CDC) and other agencies receive this information. This paper outlines what that future electronic reporting system will look like and how it will impact HAI reporting.

Risk-adjustment models for clean and colorectal surgery surgical site infection for the Spanish health system

OBJECTIVE

To develop risk-adjusted models for two quality indicators addressing surgical site infection (SSI) in clean and colorectal surgery, to be used for benchmarking and quality improvement in the Spanish National Health System.

STUDY DESIGN

A literature review was undertaken to identify candidate adjustment variables. The candidate variables were revised by clinical experts to confirm their clinical relevance to SSI; experts also offered additional candidate variables that were not identified in the literature review. Two risk-adjustment models were developed using multiple logistic regression thus allowing calculation of the adjusted indicator rates.

DATA SOURCE

The two SSI indicators, with their corresponding risk-adjustment models, were calculated from administrative databases obtained from nine public hospitals. A dataset was obtained from a 10-year period (2006-2015), and it included data from 21 571 clean surgery patients and 6325 colorectal surgery patients.

ANALYSIS METHODS

Risk-adjustment regression models were constructed using Spanish National Health System data. Models were analysed so as to prevent overfitting, then tested for calibration and discrimination and finally bootstrapped.

RESULTS

Ten adjustment variables were identified for clean surgery SSI, and 23 for colorectal surgery SSI. The final adjustment models showed fair calibration (Hosmer-Lemeshow: clean surgery χ2 = 6.56, P = 0.58; colorectal surgery χ2 = 6.69, P = 0.57) and discrimination (area under receiver operating characteristic [ROC] curve: clean surgery 0.72, 95% confidence interval [CI] 0.67-0.77; colorectal surgery 0.62, 95% CI 0.60-0.65).

CONCLUSIONS

The proposed risk-adjustment models can be used to explain patient-based differences among healthcare providers. They can be used to adjust the two proposed SSI indicators.

Designing Surveillance of Healthcare-Associated Infections in the Era of Automation and Reporting Mandates

Surveillance and feedback of infection rates to clinicians and other stakeholders is a cornerstone of healthcare-associated infection (HAI) prevention programs. In addition, HAIs are increasingly included in public reporting and payment mandates. Conventional manual surveillance methods are resource intensive and lack standardization. Developments in information technology have propelled a movement toward the use of standardized and semiautomated methods.When developing automated surveillance systems, several strategies can be chosen with regard to the degree of automation and standardization and the definitions used. Yet, the advantages of highly standardized surveillance may come at the price of decreased clinical relevance and limited preventability. The choice among (automated) surveillance approaches, therefore, should be guided by the intended aim and scale of surveillance (eg, research, in-hospital quality improvement, national surveillance, or pay-for-performance mandates), as this choice dictates subsequent methods, important performance characteristics, and suitability of the data generated for the different applications.

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.

Electronically Available Comorbid Conditions for Risk Prediction of Healthcare-Associated Clostridium difficile Infection

OBJECTIVE To analyze whether electronically available comorbid conditions are risk factors for Centers for Disease Control and Prevention (CDC)-defined, hospital-onset Clostridium difficile infection (CDI) after controlling for antibiotic and gastric acid suppression therapy use. PATIENTS Patients aged ≥18 years admitted to the University of Maryland Medical Center between November 7, 2015, and May 31, 2017. METHODS Comorbid conditions were assessed using the Elixhauser comorbidity index. The Elixhauser comorbidity index and the comorbid condition components were calculated using the International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM) codes extracted from electronic medical records. Bivariate associations between CDI and potential covariates for multivariable regression, including antibiotic use, gastric acid suppression therapy use, as well as comorbid conditions, were estimated using log binomial multivariable regression. RESULTS After controlling for antibiotic use, age, proton-pump inhibitor use, and histamine-blocker use, the Elixhauser comorbidity index was a significant risk factor for predicting CDI. There was an increased risk of 1.26 (95% CI, 1.19-1.32) of having CDI for each additional Elixhauser point added to the total Elixhauser score. CONCLUSIONS An increase in Elixhauser score is associated with CDI. Our study and other studies have shown that comorbid conditions are important risk factors for CDI. Electronically available comorbid conditions and scores like the Elixhauser index should be considered for risk-adjustment of CDC CDI rates. Infect Control Hosp Epidemiol 2018;39:297-301.

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

OBJECTIVE To determine which comorbid conditions are considered causally related to central-line associated bloodstream infection (CLABSI) and surgical-site infection (SSI) based on expert consensus. DESIGN Using the Delphi method, we administered an iterative, 2-round survey to 9 infectious disease and infection control experts from the United States. METHODS Based on our selection of components from the Charlson and Elixhauser comorbidity indices, 35 different comorbid conditions were rated from 1 (not at all related) to 5 (strongly related) by each expert separately for CLABSI and SSI, based on perceived relatedness to the outcome. To assign expert consensus on causal relatedness for each comorbid condition, all 3 of the following criteria had to be met at the end of the second round: (1) a majority (>50%) of experts rating the condition at 3 (somewhat related) or higher, (2) interquartile range (IQR)≤1, and (3) standard deviation (SD)≤1. RESULTS From round 1 to round 2, the IQR and SD, respectively, decreased for ratings of 21 of 35 (60%) and 33 of 35 (94%) comorbid conditions for CLABSI, and for 17 of 35 (49%) and 32 of 35 (91%) comorbid conditions for SSI, suggesting improvement in consensus among this group of experts. At the end of round 2, 13 of 35 (37%) and 17 of 35 (49%) comorbid conditions were perceived as causally related to CLABSI and SSI, respectively. CONCLUSIONS 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. Infect Control Hosp Epidemiol 2017;38:449-454.