First Year of Mandatory Reporting of Healthcare-Associated Infections, Pennsylvania An Infection Control—Chart Abstractor Collaboration

Julian, Kathleen G.; Brumbach, Arlene; Chicora, Michelle K.; Houlihan, Carol; Riddle, Anna M.; Umberger, Teanna; Whitener, Cynthia

doi:10.1086/507281

Cited by 30 publications

(13 citation statements)

References 5 publications

(2 reference statements)

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“…[15] However, automated surveillance may be a poor proxy for conventional HAI surveillance (particularly when utilising the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) coding), with both false positives and missed HAI events reported. [16][17][18] Computerised HAI identification algorithms that use multiple sources of information (e.g. laboratory data, ICD-10 coding and inpatient prescriptions) can achieve better sensitivity and positive predictive values (PPVs).…”

Section: Researchmentioning

confidence: 99%

Surveillance of healthcare-associated infection in hospitalised South African children: Which method performs best?

2016

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Background. In 2012, the South African (SA) National Department of Health mandated surveillance of healthcare-associated infection (HAI), but made no recommendations of appropriate surveillance methods. Methods. Prospective clinical HAI surveillance (the reference method) was conducted at Tygerberg Children's Hospital, Cape Town, from 1 May to 31 October 2015. Performance of three surveillance methods (point prevalence surveys (PPSs), laboratory surveillance and tracking of antimicrobial prescriptions) was compared with the reference method using surveillance evaluation guidelines. Factors associated with failure to detect HAI were identified by logistic regression analysis. Results. The reference method detected 417 HAIs among 1 347 paediatric hospitalisations (HAI incidence of 31/1000 patient days; 95% confidence interval (CI) 28.2 -34.2). Surveillance methods had variable sensitivity (S) and positive predictive value (PPV): PPS S = 24.9% (95% CI 21 -29.3), PPV = 100%; laboratory surveillance S = 48.4% (95% CI 43.7 -53.2), PPV = 55.2% (95% CI 50.1 -60.2); and antimicrobial prescriptions S = 66.4% (95% CI 61.8 -70.8%), PPV = 88.5% (95% CI 84.5 -91.6). Combined laboratory-antimicrobial surveillance achieved superior HAI detection (S = 84.7% (95% CI 80.9 -87.8%), PPV = 97% (95% CI 94.6 -98.4%)). Factors associated with failure to detect HAI included patient transfer (odds ratio (OR) 2.0), single HAI event (OR 2.8), age category 1 -5 years (OR 2.1) and hospitalisation in a general ward (OR 2.3). Conclusions. Repeated PPSs, laboratory surveillance and/or antimicrobial prescription tracking are feasible HAI surveillance methods for low-resource settings. Combined laboratory-antimicrobial surveillance achieved the best sensitivity and PPV. SA paediatric healthcare facilities should individualise HAI surveillance, selecting a method suited to available resources and practice context.

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Section: Researchmentioning

confidence: 99%

Surveillance of healthcare-associated infection in hospitalised South African children: Which method performs best?

2016

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“…Some authors have concluded that billing and claims data cannot be reliably used for SSI surveillance. 4,9,10 We found that 94.3% of patients identified as having an SSI by our rigorous claims algorithm also received clinically expected treatment for infection; a more conservative PPV estimate excluding culture was still very high, at 89.5%. While we could not confirm the SSIs with medical chart review, our results suggest that the claims algorithm we used to identify SSIs has very good PPV.…”

Section: Discussionmentioning

confidence: 81%

Can Additional Information Be Obtained from Claims Data to Support Surgical Site Infection Diagnosis Codes?

Warren

Nickel

Wallace

et al. 2014

Infect. Control Hosp. Epidemiol.

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OBJECTIVE. International Classification of Diseases, Ninth Revision, diagnosis codes are increasingly used to identify healthcare-associated infections, often with insufficient evidence demonstrating validity of the codes used. Absent medical record verification, we sought to confirm a claims algorithm to identify surgical site infections (SSIs) by examining the presence of clinically expected SSI treatment.METHODS. We performed a retrospective cohort study, using private insurer claims data from persons less than 65 years old with ICD-9-CM procedure or Current Procedure Terminology (CPT-4) codes for anterior cruciate ligament (ACL) reconstruction from lanuary 2004 through December 2010. SSIs occurring within 90 days after ACL reconstruction were identified by ICD-9-CM diagnosis codes. Antibiotic utilization, surgical treatment, and microbiology culture claims within 14 days of SSI codes were used as evidence to support the SSI diagnosis.RESULTS. Of 40,702 procedures, 401 (1.0%) were complicated by SSI, 172 (0.4%) of which were specifically identified as septic arthritis. Most SSIs were associated with an inpatient admission (232/401 [58%]), and/or surgical procedure(s) for treatment (250/401 [62%]). Temporally associated antibiotics, surgical treatment procedures, and cultures were present for 84% (338/401), 61% (246/401), and 59% (238/401), respectively. Only 5.7% (23/401) of procedures coded for SSI after the procedure had no antibiotics, surgical treatments, or cultures within 14 days of the SSI claims.CONCLUSIONS. More than 94% of patients identified by our claims algorithm as having an SSI received clinically expected treatment for infection, including antibiotics, surgical treatment, and culture, suggesting that this algorithm has very good positive predictive value. This method may facilitate retrospective SSI surveillance and comparison of SSI rates across facilities and providers.

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“…5–9 Studies completed before the 2008 CMS rule generally concluded that claims data overestimated the incidence of HAI compared with traditional IC methods by approximately 5 to 1. 5,6 A study by Stevenson et al 5 estimated a 15% PPV for CLABSI identified with a set of multiple ICD-9 codes compared with traditional IC.…”

Section: Discussionmentioning

confidence: 99%

“…Most authors who have studied this topic emphasize that billing data are inaccurate when compared with traditional surveillance methods. 5–9 At present, there is no measure of central line–related bloodstream infection that perfectly reflects clinical truth. Traditional IC methods have been previously criticized for elements of subjectivity and are considered a proxy measure for the true, unknown incidence.…”

mentioning

confidence: 99%

Central Line-Associated Infections as Defined by the Centers for Medicare and Medicaid Services' Hospital-Acquired Condition versus Standard Infection Control Surveillance Why Hospital Compare Seems Conflicted

Moehring

Staheli²,

Miller

et al. 2013

Infect. Control Hosp. Epidemiol.

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OBJECTIVE To evaluate the concordance of case-finding methods for central line–associated infection as defined by Centers for Medicare and Medicaid Services (CMS) hospital-acquired condition (HAC) compared with traditional infection control (IC) methods. SETTING One tertiary care and 2 community hospitals in North Carolina. PATIENTS Adult and pediatric hospitalized patients determined to have central line infection by either case-finding method. METHODS We performed a retrospective comparative analysis of infection detected using HAC versus standard IC central line–associated bloodstream infection surveillance from October 1, 2007, through December 31, 2009. One billing and 2 IC databases were queried and matched to determine the number and concordance of cases identified by each method. Manual review of 25 cases from each discordant category was performed. Sensitivity and positive predictive value (PPV) were calculated using IC as criterion standard. RESULTS A total of 1,505 cases were identified: 844 by International Classification of Diseases, Ninth Revision (ICD-9), and 798 by IC. A total of 204 cases (24%) identified by ICD-9 were deemed not present at hospital admission by coders. Only 112 cases (13%) were concordant. HAC sensitivity was 14% and PPV was 55% compared with IC. Concordance was low regardless of hospital type. Primary reasons for discordance included differences in surveillance and clinical definitions, clinical uncertainty, and poor documentation. CONCLUSIONS The case-finding method used by CMS HAC and the methods used for traditional IC surveillance frequently do not agree. This can lead to conflicting results when these 2 measures are used as hospital quality metrics.

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First Year of Mandatory Reporting of Healthcare-Associated Infections, Pennsylvania An Infection Control—Chart Abstractor Collaboration

Cited by 30 publications

References 5 publications

Surveillance of healthcare-associated infection in hospitalised South African children: Which method performs best?

Surveillance of healthcare-associated infection in hospitalised South African children: Which method performs best?

Can Additional Information Be Obtained from Claims Data to Support Surgical Site Infection Diagnosis Codes?

Central Line-Associated Infections as Defined by the Centers for Medicare and Medicaid Services' Hospital-Acquired Condition versus Standard Infection Control Surveillance Why Hospital Compare Seems Conflicted

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