scholarly journals Analysis of National Healthcare Safety Network Clostridioides difficile Infection Standardized Infection Ratio by Test Type

2020 ◽  
Vol 41 (S1) ◽  
pp. s87-s89
Author(s):  
Qunna Li ◽  
Andrea Benin ◽  
Alice Guh ◽  
Margaret Dudeck ◽  
Katherine Allen-Bridson ◽  
...  
Keyword(s):  
Risk Adjustment ◽  
Healthcare Facility ◽  
Directional Pattern ◽  
Incidence Rates ◽  
Nucleic Acid Amplification ◽  
Test Type ◽  
National Healthcare ◽  
Clostridioides Difficile ◽  
National Healthcare Safety Network ◽  
The Mean

Background: The National Healthcare Safety Network (NHSN) has used positive laboratory tests for surveillance of Clostridioides difficile infection (CDI) LabID events since 2009. Typically, CDIs are detected using enzyme immunoassays (EIAs), nucleic acid amplification tests (NAATs), or various test combinations. The NHSN uses a risk-adjusted, standardized infection ratio (SIR) to assess healthcare facility-onset (HO) CDI. Despite including test type in the risk adjustment, some hospital personnel and other stakeholders are concerned that NAAT use is associated with higher SIRs than EIA use. To investigate this issue, we analyzed NHSN data from acute-care hospitals for July 1, 2017, through June 30, 2018. Methods: Calendar quarters where CDI test type was reported as NAAT (includes NAAT, glutamate dehydrogenase (GDH)+NAAT and GDH+EIA followed by NAAT if discrepant) or EIA (includes EIA and GDH+EIA) were selected. HO-CDI SIRs were calculated for facility-wide inpatient locations. We conducted the following 2 analyses: (1) Among hospitals that did not switch their test type, we compared the distribution of HO incident rates and SIRs by those reporting NAAT versus EIA. (2) Among hospitals that switched their test type, we selected quarters with a stable switch pattern of 2 consecutive quarters of each of EIA and NAAT (categorized as EIA-to-NAAT or NAAT-to-EIA). Pooled semiannual SIRs for EIA and NAAT were calculated, and a paired t test was used to evaluate the difference in SIRs by switch pattern. Results: Most hospitals did not switch test types (3,242, 89%), and 2,872 (89%) reported sufficient data to calculate an SIR, with 2,444 (85%) using NAAT. The crude pooled HO CDI incidence rates for hospitals using EIAs clustered at the lower end of the histogram versus rates for NAATs (Fig. 1). The SIR distributions, both NAATs and EIAs, overlapped substantially and covered a similar range of SIR values (Fig. 1). Among hospitals with a switch pattern, hospitals were equally likely to have an increase or decrease in their SIRs (Fig. 2). The mean SIR difference for the 42 hospitals switching from EIA to NAAT was 0.048 (95% CI, −0.189 to 0.284; P = .688). The mean SIR difference for the 26 hospitals switching from NAAT to EIA was 0.162 (95% CI, −0.048 to 0.371; P = .124). Conclusions: The pattern of SIR distribution for both NAAT and EIA substantiate the soundness of the NHSN’s risk adjustment for CDI test types. Switching test type did not produce a consistent directional pattern in SIR that was statistically significant.Funding: NoneDisclosures: None

scholarly journals Analysis of National Healthcare Safety Network Clostridioides difficile Infection Standardized Infection Ratio by Test Type

2020 ◽  
Vol 41 (S1) ◽  
pp. s116-s118
Author(s):  
Qunna Li ◽  
Andrea Benin ◽  
Alice Guh ◽  
Margaret A. Dudeck ◽  
Katherine Allen-Bridson ◽  
...  
Keyword(s):  
Risk Adjustment ◽  
Healthcare Facility ◽  
Directional Pattern ◽  
Incidence Rates ◽  
Nucleic Acid Amplification ◽  
Test Type ◽  
Clostridioides Difficile ◽  
Clostridioides Difficile Infection ◽  
The Mean ◽  
The Difference

Background: The NHSN has used positive laboratory tests for surveillance of Clostridioides difficile infection (CDI) LabID events since 2009. Typically, CDIs are detected using enzyme immunoassays (EIAs), nucleic acid amplification tests (NAATs), or various test combinations. The NHSN uses a risk-adjusted, standardized infection ratio (SIR) to assess healthcare facility-onset (HO) CDI. Despite including test type in the risk adjustment, some hospital personnel and other stakeholders are concerned that NAAT use is associated with higher SIRs than are EIAs. To investigate this issue, we analyzed NHSN data from acute-care hospitals for July 1, 2017 through June 30, 2018. Methods: Calendar quarters for which CDI test type was reported as NAAT (includes NAAT, glutamate dehydrogenase (GDH)+NAAT and GDH+EIA followed by NAAT if discrepant) or EIA (includes EIA and GDH+EIA) were selected. HO CDI SIRs were calculated for facility-wide inpatient locations. We conducted the following analyses: (1) Among hospitals that did not switch their test type, we compared the distribution of HO incident rates and SIRs by those reporting NAAT vs EIA. (2) Among hospitals that switched their test type, we selected quarters with a stable switch pattern of 2 consecutive quarters of each of EIA and NAAT (categorized as pattern EIA-to-NAAT or NAAT-to-EIA). Pooled semiannual SIRs for EIA and NAAT were calculated, and a paired t test was used to evaluate the difference of SIRs by switch pattern. Results: Most hospitals did not switch test types (3,242, 89%), and 2,872 (89%) reported sufficient data to calculate SIRs, with 2,444 (85%) using NAAT. The crude pooled HO CDI incidence rates for hospitals using EIA clustered at the lower end of the histogram versus rates for NAAT (Fig. 1). The SIR distributions of both NAAT and EIA overlapped substantially and covered a similar range of SIR values (Fig. 1). Among hospitals with a switch pattern, hospitals were equally likely to have an increase or decrease in their SIR (Fig. 2). The mean SIR difference for the 42 hospitals switching from EIA to NAAT was 0.048 (95% CI, −0.189 to 0.284; P = .688). The mean SIR difference for the 26 hospitals switching from NAAT to EIA was 0.162 (95% CI, −0.048 to 0.371; P = .124). Conclusions: The pattern of SIR distributions of both NAAT and EIA substantiate the soundness of NHSN risk adjustment for CDI test types. Switching test type did not produce a consistent directional pattern in SIR that was statistically significant.Disclosures: NoneFunding: None

scholarly journals Potential for the current National Healthcare Safety Network (NHSN) >3 days after admission definition of laboratory-identified, healthcare-facility–onset, Clostridioides difficile infection (HO-CDI) to overestimate rates

2020 ◽  
Vol 41 (4) ◽  
pp. 467-468
Author(s):  
Shruti Puri ◽  
Heather Y. Hughes ◽  
Monica D. McCrackin ◽  
Robert Williford ◽  
Mulugeta Gebregziabher ◽  
...  
Keyword(s):  
Healthcare Facility ◽  
National Healthcare ◽  
Clostridioides Difficile ◽  
National Healthcare Safety Network ◽  
Positive Stool ◽  
Clostridioides Difficile Infection ◽  
Stool Samples ◽  
Definition Of

AbstractHealthcare-facility–onset C.difficile LabID events are defined as positive stool samples collected >3 days after hospitalization. Using a definition of >72 hours, we found that 84 of 1013 cases (8.3%) identified as C. difficile LabID events were collected between 48 and 72 hours after admission.

Reductions in positive Clostridioides difficile events reportable to National Healthcare Safety Network (NHSN) with adoption of reflex enzyme immunoassay (EIA) testing in 13 Atlanta hospitals

2021 ◽  
pp. 1-4
Author(s):  
Dana Goodenough ◽  
Samantha Sefton ◽  
Elizabeth Overton ◽  
Elizabeth Smith ◽  
Colleen S. Kraft ◽  
...  
Keyword(s):  
Nucleic Acid ◽  
Enzyme Immunoassay ◽  
Infection Prevention ◽  
Nucleic Acid Amplification Test ◽  
Nucleic Acid Amplification ◽  
National Healthcare ◽  
Clostridioides Difficile ◽  
Performance Metric ◽  
National Healthcare Safety Network ◽  
Testing Practice

Abstract In total, 13 facilities changed C. difficile testing to reflexive testing by enzyme immunoassay (EIA) only after a positive nucleic acid-amplification test (NAAT); the standardized infection ratio (SIR) decreased by 46% (range, −12% to −71% per hospital). Changing testing practice greatly influenced a performance metric without changing C. difficile infection prevention practice.

scholarly journals Trends in Hospital Onset Clostridioides difficile Infection Incidence, National Healthcare Safety Network, 2010–2018

2020 ◽  
Vol 41 (S1) ◽  
pp. s53-s54
Author(s):  
Yi Mu ◽  
Margaret Dudeck ◽  
Karen Jones ◽  
Qunna Li ◽  
Minn Soe ◽  
...  
Keyword(s):  
Average Length ◽  
Rate Of Change ◽  
Observation Unit ◽  
Test Type ◽  
Quality Reporting ◽  
National Healthcare ◽  
Clostridioides Difficile ◽  
National Healthcare Safety Network ◽  
National Priority ◽  
Over Time

Background:Clostridioides difficile infection (CDI) is one of the most common laboratory-identified (LabID) healthcare-associated events reported to the National Healthcare Safety Network (NHSN). CDI prevention remains a national priority, and efforts to reduce infection burden and improve antibiotic stewardship continue to expand across the healthcare spectrum. Beginning in 2013, the Centers for Medicare and Medicaid Services (CMS) required acute-care hospitals participating in CMS’ Inpatient Quality Reporting program to report CDI LabID data to NHSN and, in 2015, extended this reporting requirement to emergency departments (ED) and 24-hour observation units. To assess national progress, we evaluated changes in hospital onset CDI (HO-CDI) incidence during 2010–2018. Methods: Cases of HO-CDI were reported to NHSN by hospitals using the NHSN’s LabID criteria. Generalized linear mixed-effects modeling was used to assess trends of HO-CDI by treating the hospital as a random intercept to account for the correlation of the repeated responses over time. The data were summarized at the quarterly level, the main effect was time, and the covariates of interest were the following: CDI test type, inpatient community-onset (CO) infection rate, hospital type, average length of stay, medical school affiliation, number of beds, number of ICU beds, number of infection control professionals, presence of an ED or observation unit , and an indicator for 2015 to account for CDI protocol changes that required hospitals to conduct surveillance in both inpatient and ED or observation unit setting. Results: During 2010–2013, the number of hospitals reporting CDI increased and then stabilized after 2013 (Table 1). Crude HO-CDI rates decreased over time, except for an increase in 2015 and steeper reduction thereafter. (Table 2). During 2010–2014, the adjusted quarterly rate of change was −0.45% (95% CI, −0.57% to −0.33%; P < .0001). The rate of reduction was smaller in 2010–2014 compared to those of 2015–2018 (−2.82%; 95% CI, −3.10% to −2.54%; P < .0001). Compared to 2014, the adjusted rate in 2015 increased by 79.14% (95% CI, 72.42%–86.11%; P < .0001). Conclusions: The number of hospitals reporting CDI LabID data grew substantially in 2013 as a result of the CMS requirement for reporting. Adjusted HO-CDI rates decreased over time, with a rate hike in the year of 2015 and a rapid decrease thereafter. The increase in 2015 may be explained by changes in the NHSN CDI surveillance protocol and better test type classification in later years. Overall decreases in HO-CDI rates may be influenced by prevention strategies.Funding: NoneDisclosures: None

scholarly journals 875. Pseudo-outbreak of Clostridioides (Clostridium) difficile amongst post-operative Oncology Patients

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S475-S475
Author(s):  
Daniel Kagedan ◽  
Roderich Schwarz ◽  
Jillianna Wasiura ◽  
Nikolaos Almyroudis ◽  
Robin Patel ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Healthcare Facility ◽  
Whole Genome ◽  
Research Support ◽  
National Healthcare ◽  
Clostridioides Difficile ◽  
National Healthcare Safety Network ◽  
Allelic Differences ◽  
Inpatient Units

Abstract Background Clostridioides difficile infection rates are subject to infection prevention surveillance as a quality measure within the hospital setting. A large spike in Clostridioides difficile infections in post-operative patients, the majority of whom were gastrointestinal surgery (GIS) patients, was noted within a six month period (June through November 2019) at our comprehensive cancer center. These patients had been housed in one of two inpatient units and there was appropriate concern that this represented a C. difficile outbreak possibly related some type of infection control breach. Methods In an effort to query case relatedness, whole genome sequencing was performed using Illumina MiSeq instrumentation and chemistry with Illumina Nextera XT library chemistry. Assembly and core genome multilocus sequence typing analysis were performed with Ridom SeqSphere+ software. Cases were classified as community or hospital acquired based on the National Healthcare Safety Network (NHSN) definitions. Results There were 23 samples submitted for possible whole genome sequencing (WGS). 5 samples were unable to be grown therefore WGS was not completed; 16 were found to be unrelated (51 or more allelic differences); 2 of the 18 isolates were found to be possibly related (7 to 50 allelic differences). There were no isolates found to be definitively related (zero to 6 allelic differences). Conclusion Given the overwhelming unrelatedness of the isolates via whole genome sequencing, this increase of C. difficile cases, identified by routine surveillance within two inpatient units, was determined to be representative of a pseudo-outbreak rather than an outbreak. This study has implications on public health reporting. National Healthcare Safety Network definitions are used to identify healthcare facility-onset C. difficile infections (CDI). The majority of cases in this study met the definition of healthcare facility-onset, and thus were reported as such, despite being genetically unrelated. This raises the concern that a significant percentage of C. difficile infections may be currently misclassified as hospital-associated and this may have negative, unfair consequences for hospitals, such as implications on reimbursement. Disclosures Robin Patel, MD, Accelerate Diagnostics (Grant/Research Support)CD Diagnostics (Grant/Research Support)Contrafect (Grant/Research Support)Curetis (Consultant)GenMark Diagnostics (Consultant)Heraeus Medical (Consultant)Hutchison Biofilm Medical Solutions (Grant/Research Support)Merck (Grant/Research Support)Next Gen Diagnostics (Consultant)PathoQuest (Consultant)Qvella (Consultant)Samsung (Other Financial or Material Support, Dr. Patel has a patent on Bordetella pertussis/parapertussis PCR issued, a patent on a device/method for sonication with royalties paid by Samsung to Mayo Clinic, and a patent on an anti-biofilm substance issued.)Selux Dx (Consultant)Shionogi (Grant/Research Support)Specific Technologies (Consultant)

scholarly journals Diagnostic Stewardship Effectively Reduces Healthcare-Onset Clostridioides difficile Infections and Concurrent Laxative Use

2020 ◽  
Vol 41 (S1) ◽  
pp. s189-s191
Author(s):  
Dipesh Solanky ◽  
Ian Drobish ◽  
Derek Juang ◽  
Scott Johns ◽  
Sanjay Mehta ◽  
...  
Keyword(s):  
Enteral Feeding ◽  
Demographic Data ◽  
Healthcare Facility ◽  
The United States ◽  
Nucleic Acid Amplification ◽  
Fiscal Year ◽  
Medication Cost ◽  
Laxative Use ◽  
Clostridioides Difficile ◽  
Test Ordering

Background:Clostridioides difficile infection (CDI) accounts for >500,000 community-, nursing-, and hospital-acquired infections (HAIs), as well as 15,000–30,000 deaths, and =$4.8 billion in the United States annually. C. difficile toxin B gene nucleic acid amplification testing (NAAT) cannot distinguish between active CDI and colonization, particularly in the setting of laxative use or enteral feeding. Lack of judicious testing can result in the incorrect diagnosis of CDI, unnecessary CDI treatment, increased costs, and falsely augmented HAI rates. Like many healthcare facilities, the VA San Diego Healthcare System (VASDHS) solely utilizes C. difficile NAAT for CDI diagnosis. The aim of this study was to implement and evaluate a facility-wide initiative at the VASDHS to reduce healthcare onset, healthcare facility associated CDI (HO-HCFA CDI), including the use of a test ordering algorithm. Methods: From fiscal year (FY) 2015–2018, various measures were implemented including a hand hygiene initiative, reduction in fluoroquinolone usage, prompt isolation of patients with CDI, thorough terminal cleaning of rooms, and, lastly, a test-ordering algorithm starting FY2018. A retrospective study was performed to assess VASDHS HO-HCFA CDI case incidence, risk factors for infection, laxative or enteral feeding use at the time of testing, and CDI treatment. Results: Patient demographic data, medical history, CDI history, laxative use, treatment, and cost of CDI treatment were reviewed. From 2015 to 2018, 127 cases of HO-HCFA CDI were identified. The total number of HO-HCFA CDI cases and medication cost for CDI treatment were dramatically reduced from 2017 to 2018 following implementation of the test-ordering algorithm (Table 1, Fig. 1). This trend corresponded to a significant reduction in median HO-HCFA CDI cases per month (P = .02), medication cost of CDI treatment (P = .02), and proton pump inhibitor (PPI) use at the time of testing (P = .01). The number of positive HO-HCFA CDI cases associated with laxative use or escalation at the time of CDI testing (accounting for those on chronic laxatives) also decreased across the study period—most dramatically from 2015 vs 2016 (20 vs 14) and 2017 vs 2018 (11 vs 4) (Table 1). Conclusions: At the VASDHS, diagnostic stewardship of C. difficile NAAT with the use of a test-ordering algorithm significantly reduced HO-HCFA CDI incidence and treatment cost. This trend also corresponded with significantly less PPI use at the time of testing and reduced detection of colonization among patients with laxative-induced diarrhea. Diagnostic stewardship may serve as an effective tool to correctly diagnose and treat HO-HCFA CDI, while significantly reducing treatment costs.Funding: NoneDisclosures: None

Using diagnostic stewardship to reduce rates, healthcare expenditures and accurately identify cases of hospital-onset Clostridioides difficile infection

2020 ◽  
Vol 42 (1) ◽  
pp. 51-56
Author(s):  
Dipesh Solanky ◽  
Derek K. Juang ◽  
Scott T. Johns ◽  
Ian C. Drobish ◽  
Sanjay R. Mehta ◽  
...  
Keyword(s):  
Healthcare Facility ◽  
Nucleic Acid Amplification ◽  
Fiscal Year ◽  
Hospital Acquired Infection ◽  
Laxative Use ◽  
Clostridioides Difficile ◽  
Hospitalization Costs ◽  
Clostridioides Difficile Infection ◽  
Hospital Acquired ◽  
Hospital Days

AbstractObjective:Lack of judicious testing can result in the incorrect diagnosis of Clostridioides difficile infection (CDI), unnecessary CDI treatment, increased costs and falsely augmented hospital-acquired infection (HAI) rates. We evaluated facility-wide interventions used at the VA San Diego Healthcare System (VASDHS) to reduce healthcare-onset, healthcare-facility–associated CDI (HO-HCFA CDI), including the use of diagnostic stewardship with test ordering criteria.Design:We conducted a retrospective study to assess the effectiveness of measures implemented to reduce the rate of HO-HCFA CDI at the VASDHS from fiscal year (FY)2015 to FY2018.Interventions:Measures executed in a stepwise fashion included a hand hygiene initiative, prompt isolation of CDI patients, enhanced terminal room cleaning, reduction of fluoroquinolone and proton-pump inhibitor use, laboratory rejection of solid stool samples, and lastly diagnostic stewardship with C. difficile toxin B gene nucleic acid amplification testing (NAAT) criteria instituted in FY2018.Results:From FY2015 to FY2018, 127 cases of HO-HCFA CDI were identified. All rate-reducing initiatives resulted in decreased HO-HCFA cases (from 44 to 13; P ≤ .05). However, the number of HO-HCFA cases (34 to 13; P ≤ .05), potential false-positive testing associated with colonization and laxative use (from 11 to 4), hospital days (from 596 to 332), CDI-related hospitalization costs (from $2,780,681 to $1,534,190) and treatment cost (from $7,158 vs $1,476) decreased substantially following the introduction of diagnostic stewardship with test criteria from FY2017 to FY2018.Conclusions:Initiatives to decrease risk for CDI and diagnostic stewardship of C. difficile stool NAAT significantly reduced HO-HCFA CDI rates, detection of potential false-positives associated with laxative use, and lowered healthcare costs. Diagnostic stewardship itself had the most dramatic impact on outcomes observed and served as an effective tool in reducing HO-HCFA CDI rates.

Device-Associated Infections among Neonatal Intensive Care Unit Patients: Incidence and Associated Pathogens Reported to the National Healthcare Safety Network, 2006–2008

2012 ◽  
Vol 33 (12) ◽  
pp. 1200-1206 ◽  
Author(s):  
Susan N. Hocevar ◽  
Jonathan R. Edwards ◽  
Teresa C. Horan ◽  
Gloria C. Morrell ◽  
Martha Iwamoto ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Neonatal Intensive Care Unit ◽  
Neonatal Intensive Care ◽  
Bloodstream Infections ◽  
Incidence Rates ◽  
General Hospitals ◽  
Weight Category ◽  
National Healthcare ◽  
National Healthcare Safety Network

Objective.To describe rates and pathogen distribution of device-associated infections (DAIs) in neonatal intensive care unit (NICU) patients and compare differences in infection rates by hospital type (children's vs general hospitals).Patients and Setting.Neonates in NICUs participating in the National Healthcare Safety Network from 2006 through 2008.Methods.We analyzed central line–associated bloodstream infections (CLABSIs), umbilical catheter–associated bloodstream infections (UCABs), and ventilator-associated pneumonia (VAP) among 304 NICUs. Differences in pooled mean incidence rates were examined using Poisson regression; nonparametric tests for comparing medians and rate distributions were used.Results.Pooled mean incidence rates by birth weight category (750 g or less, 751–1,000 g, 1,001–1,500 g, 1,501–2,500 g, and more than 2,500 g, respectively) were 3.94, 3.09, 2.25, 1.90, and 1.60 for CLABSI; 4.52, 2.77, 1.70, 0.91, and 0.92 for UCAB; and 2.36, 2.08, 1.28, 0.86, and 0.72 for VAP. When rates of infection between hospital types were compared, only pooled mean VAP rates were significantly lower in children's hospitals than in general hospitals among neonates weighing 1,000 g or less; no significant differences in medians or rate distributions were noted. Pathogen frequencies were coagulase-negative staphylococci (28%), Staphylococcus aureus (19%), and Candida species (13%) for bloodstream infections and Pseudomonas species (16%), S. aureus (15%), and Klebsiella species (14%) for VAP. Of 673 S. aureus isolates with susceptibility results, 33% were methicillin resistant.Conclusions.Neonates weighing 750 g or less had the highest DAI incidence. With the exception of VAP, pooled mean NICU incidence rates did not differ between children's and general hospitals. Pathogens associated with these infections can pose treatment challenges; continued efforts at prevention need to be applied to all NICU settings.

scholarly journals An Assessment of 2016 National Healthcare Safety Network (NHSN) and National Electronic Disease Surveillance System (NEDSS) Clostridium difficile Infections (CDI) in Nebraska

2017 ◽  
Vol 4 (suppl_1) ◽  
pp. S399-S399
Author(s):  
Caitlin Pedati ◽  
Madison Sullivan ◽  
Margaret Drake ◽  
Alison Keyser ◽  
Tom Safranek ◽  
...  
Keyword(s):  
Clostridium Difficile ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Healthcare Facility ◽  
Data Sources ◽  
National Healthcare ◽  
Disease Surveillance System ◽  
Department Of Health ◽  
National Healthcare Safety Network ◽  
Community Onset

Abstract Background In 2016 all acute care hospitals, inpatient rehab facilities, and PPS-exempt cancer facilities in Nebraska were required to report laboratory identified (LabID) Clostridium difficile infections (CDIs) to the National Healthcare Safety Network (NHSN). Test results indicating CDIs must be reported to the Nebraska Department of Health and Human Services (NDHHS) via the National Electronic Disease Surveillance System (NEDSS). NHSN and NEDSS represent unique sources of CDI reports in Nebraska. Methods The NHSN Nebraska database was queried for CDIs reported in 2016. All lab tests indicating a CDI in 2016 were extracted from NEDSS. These extracts were analyzed to assess descriptive epidemiologic variables and compared for differences. Results In 2016 there were 1,546 CDI LabID events reported to NHSN Nebraska from 28 facilities. There were 249 outpatient CDIs and 1,297 inpatient CDIs. Infections were further characterized as community-onset (N = 773), community-onset, healthcare facility associated (N = 206), and hospital onset (N = 567). An average of 128 CDIs were reported per month (range: 111–155). In 2016 there were 2,177 lab results indicating a CDI reported to NEDSS among Nebraska residents from 42 facilities. Patient ages ranged from 4 months to 104 years (mean = 58 years). An average of 181 CDIs were reported per month (range: 151–218). Comparison of the two data sources found 781 reports among 591 unique patients at 11 facilities that were made to NHSN and were not in NEDSS. Additionally, there were 1,092 reports from 931 unique patients at 12 facilities that were made to NEDSS and should have been made to NHSN but were not. There were 9 shared facilities that accounted for the majority of these discrepancies. Conclusion NHSN and NEDSS represent two unique data sources that allow for a more comprehensive assessment of CDIs. The number and type of facility that report to each system is slightly different but there is some overlap. Therefore, this comparison allows for detection of a greater number of reports overall and also provides an opportunity for data validation. This assessment identified discrepancies in reporting among 9 facilities that can be targeted for further collaborative efforts to improve CDI reporting and management in Nebraska. Disclosures All authors: No reported disclosures.

scholarly journals Problematic Risk Adjustment in National Healthcare Safety Network Measures

2019 ◽  
Vol 35 (3) ◽  
pp. 205-212 ◽  
Author(s):  
Richard L. Fuller ◽  
John S. Hughes ◽  
Graham Atkinson ◽  
Barbara S. Aubry
Keyword(s):  
Risk Adjustment ◽  
Claims Data ◽  
Fee For Service ◽  
Risk Models ◽  
Nursing Unit ◽  
National Healthcare ◽  
Hospital Variation ◽  
Hospital Intensive Care Unit ◽  
National Healthcare Safety Network ◽  
Extreme Variation

This article reviews the risk-adjustment models underpinning the National Healthcare Safety Network (NHSN) standardized infection ratios. After first describing the models, the authors focus on hospital intensive care unit (ICU) designation as a variable employed across the various risk models. The risk-adjusted frequency with which ICU services are reported in Medicare fee-for-service claims data was compared as a proxy for determining whether reporting of ICU days is similar across hospitals. Extreme variation was found in the reporting of ICU utilization among admissions for congestive heart failure, ranging from 25% in the lowest admission hospital quartile to 95% in the highest. The across-hospital variation in reported ICU utilization was found to be unrelated to patient severity. Given that such extreme variation appears in a designation of ICU versus non-ICU utilization, the NHSN risk-adjustment models’ dependence on nursing unit designation should be a cause for concern.

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