Hostname: page-component-848d4c4894-wg55d Total loading time: 0 Render date: 2024-05-06T01:10:02.378Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluation of the National Healthcare Safety Network standardized infection ratio risk adjustment for healthcare-facility-onset Clostridioides difficile infection in intensive care, oncology, and hematopoietic cell transplant units in general acute-care hospitals

Published online by Cambridge University Press:  13 February 2020

Christopher R. Polage Open the ORCID record for Christopher R. Polage [Opens in a new window] ,
Kathleen A. Quan ,
Keith Madey ,
Frank E. Myers ,
Debbra A. Wightman ,
Sneha Krishna ,
Jonathan D. Grein ,
Laurel Gibbs ,
Deborah Yokoe  and
Shannon C. Mabalot
...Show all authors
Christopher R. Polage*
Affiliation:
Department of Pathology and Laboratory Medicine, University of California Davis School of Medicine, Sacramento, California Division of Infectious Diseases, Department of Internal Medicine, University of California Davis School of Medicine, Sacramento, California Department of Pathology, Duke University School of Medicine, Durham, North Carolina
Kathleen A. Quan
Affiliation:
Epidemiology and Infection Prevention, University of California Irvine Health, Orange, California
Keith Madey
Affiliation:
Epidemiology and Infection Prevention, University of California Irvine Health, Orange, California
Frank E. Myers
Affiliation:
Infection Prevention and Clinical Epidemiology Unit, University of California San Diego Health, San Diego, California
Debbra A. Wightman
Affiliation:
Infection Prevention and Clinical Epidemiology Unit, University of California San Diego Health, San Diego, California
Sneha Krishna
Affiliation:
Hospital Epidemiology, Cedars-Sinai Medical Center, Los Angeles, California
Jonathan D. Grein
Affiliation:
Hospital Epidemiology, Cedars-Sinai Medical Center, Los Angeles, California
Laurel Gibbs
Affiliation:
Department of Hospital Epidemiology and Infection Prevention, University of California San Francisco Health, San Francisco, California
Deborah Yokoe
Affiliation:
Department of Hospital Epidemiology and Infection Prevention, University of California San Francisco Health, San Francisco, California Division of Infectious Diseases, Department of Medicine, University of California San Francisco School of Medicine, San Francisco, California
Shannon C. Mabalot
Affiliation:
Infection Prevention and Clinical Epidemiology, Sharp Memorial Hospital, Sharp HealthCare, San Diego, California
Raymond Chinn
Affiliation:
Infection Prevention and Clinical Epidemiology, Sharp Memorial Hospital, Sharp HealthCare, San Diego, California
Amy Hallmark
Affiliation:
Clinical Epidemiology and Infection Prevention, Ronald Reagan UCLA Medical Center, University of California Los Angeles Health, Los Angeles, California
Zachary Rubin
Affiliation:
Clinical Epidemiology and Infection Prevention, Ronald Reagan UCLA Medical Center, University of California Los Angeles Health, Los Angeles, California Division of Infectious Diseases, Department of Medicine, David Geffen UCLA School of Medicine, Los Angeles, California
Michael Fontenot
Affiliation:
Hospital Epidemiology and Infection Prevention, University of California Davis Health System, Sacramento, California
Stuart Cohen
Affiliation:
Division of Infectious Diseases, Department of Internal Medicine, University of California Davis School of Medicine, Sacramento, California Hospital Epidemiology and Infection Prevention, University of California Davis Health System, Sacramento, California
David Birnbaum
Affiliation:
School of Population and Public Health, Principal Applied Epidemiology, University of British Columbia, British Columbia
Susan S. Huang
Affiliation:
Epidemiology and Infection Prevention, University of California Irvine Health, Orange, California Division of Infectious Diseases, Department of Internal Medicine, University of California Irvine School of Medicine, Orange, California Health Policy Research Institute University of California Irvine School of Medicine, Orange, California
Francesca J. Torriani
Affiliation:
Infection Prevention and Clinical Epidemiology Unit, University of California San Diego Health, San Diego, California Division of Infectious Diseases, Department of Medicine, University of California San Diego, La Jolla, California
*
Author for correspondence: Christopher R. Polage, E-mail: christopher.polage@duke.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective:

To evaluate the National Health Safety Network (NHSN) hospital-onset Clostridioides difficile infection (HO-CDI) standardized infection ratio (SIR) risk adjustment for general acute-care hospitals with large numbers of intensive care unit (ICU), oncology unit, and hematopoietic cell transplant (HCT) patients.

Design:

Retrospective cohort study.

Setting:

Eight tertiary-care referral general hospitals in California.

Methods:

We used FY 2016 data and the published 2015 rebaseline NHSN HO-CDI SIR. We compared facility-wide inpatient HO-CDI events and SIRs, with and without ICU data, oncology and/or HCT unit data, and ICU bed adjustment.

Results:

For these hospitals, the median unmodified HO-CDI SIR was 1.24 (interquartile range [IQR], 1.15–1.34); 7 hospitals qualified for the highest ICU bed adjustment; 1 hospital received the second highest ICU bed adjustment; and all had oncology-HCT units with no additional adjustment per the NHSN. Removal of ICU data and the ICU bed adjustment decreased HO-CDI events (median, −25%; IQR, −20% to −29%) but increased the SIR at all hospitals (median, 104%; IQR, 90%–105%). Removal of oncology-HCT unit data decreased HO-CDI events (median, −15%; IQR, −14% to −21%) and decreased the SIR at all hospitals (median, −8%; IQR, −4% to −11%).

Conclusions:

For tertiary-care referral hospitals with specialized ICUs and a large number of ICU beds, the ICU bed adjustor functions as a global adjustment in the SIR calculation, accounting for the increased complexity of patients in ICUs and non-ICUs at these facilities. However, the SIR decrease with removal of oncology and HCT unit data, even with the ICU bed adjustment, suggests that an additional adjustment should be considered for oncology and HCT units within general hospitals, perhaps similar to what is done for ICU beds in the current SIR.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 41 , Issue 4 , April 2020 , pp. 404 - 410
Copyright
© 2020 by The Society for Healthcare Epidemiology of America. All rights reserved

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

a

Authors of equal contribution.

PREVIOUS PRESENTATION: This work was presented as an oral abstract at IDWeek 2019, on October 3, 2019, in Washington, DC.

References

Magill, SS, Edwards, JR, Bamberg, W, et al.Multistate point-prevalence survey of health care-associated infections. N Engl J Med 2014;370:1198–1208.CrossRefGoogle ScholarPubMed
Dudeck, MAWL, Malpiedi, PJ, Edwards, JR, Peterson, KD, Sievert, DM. Risk adjustment for healthcare-facility–onset C. difficile and MRSA bacteremia laboratory-identified event reporting in NHSN. Centers for Disease Control and Prevention website. https://www.cdc.gov/nhsn/PDFs/mrsa-cdi/RiskAdjustment-MRSA-CDI.pdf. Published 2013. Accessed August 4, 2019.Google Scholar
McDonald, LC, Coignard, B, Dubberke, E, et al.Recommendations for surveillance of Clostridium difficile–associated disease. Infect Control Hosp Epidemiol 2007;28:140–145.CrossRefGoogle ScholarPubMed
Centers for Medicare & Medicaid Services, Department of Health and Human Services. 42 CFR Parts 412, 413, 415, et al.Medicare program; hospital inpatient prospective payment systems for acute care hospitals and the long term care hospital prospective payment system changes and FY 2011 rates; provider agreements and supplier approvals; and hospital conditions of participation for rehabilitation and respiratory care services; medicaid program: accreditation for providers of inpatient psychiatric services; final rule 2010. Federal Register 75(157):50041–50681. National Archives and Records Administration website. https://www.govinfo.gov/content/pkg/FR-2010-08-16/pdf/2010-19092.pdf. Published 2010. Accessed August 4, 2019.Google Scholar
Centers for Medicare & Medicaid Services, Department of Health and Human Services. 42 CFR Parts 405, 412, 413, and 489. Medicare program; hospital inpatient prospective payment systems for acute care hospitals and the longterm care hospital prospective payment system and policy changes and fiscal year 2017 rates; quality reporting requirements for specific providers; graduate medical education; hospital notification procedures applicable to beneficiaries receiving observation services; technical changes relating to costs to organizations and medicare cost reports; finalization of interim final rules with comment period on LTCH PPS payments for severe wounds, modifications of limitations on redesignation by the medicare geographic classification review board, and extensions of payments to MDHs and low-volume hospitals. Federal Register 81(162):56762-7345. National Archives and Records Administration website. https://www.govinfo.gov/content/pkg/FR-2016-08-22/pdf/2016-18476.pdf. Published 2016. Accessed August 4, 2019.Google Scholar
National Healthcare Safety Network (NHSN). The NHSN standardized infection ratio (SIR): a guide to the SIR. Centers for Disease Control and Prevention website. https://www.cdc.gov/nhsn/pdfs/ps-analysis-resources/nhsn-sir-guide.pdf. Published 2019. Accessed August 4, 2019.Google Scholar
Dudeck, MA. Updating the National risk-adjustment of HAI data. Centers for Disease Control and Prevention website. https://www.cdc.gov/nhsn/pdfs/training/2016/Updating-National-Risk-Adjustment-Dudeck.pdf. Published 2016. Accessed August 4, 2019.Google Scholar
Marra, AR, Edmond, MB, Ford, BA, Herwaldt, LA, Algwizani, AR, Diekema, DJ. Failure of risk-adjustment by test method for C. difficile laboratory-identified event reporting. Infect Control Hosp Epidemiol 2017;38:109–111.CrossRefGoogle ScholarPubMed
National Healthcare Safety Network (NHSN). Paving the path forward: 2015 rebaseline. Centers for Disease Control and Prevention website. https://www.cdc.gov/nhsn/2015rebaseline/index.html. Published 2015. Accessed August 4, 2019.Google Scholar
Wallace, DJ, Seymour, CW, Kahn, JM. Hospital-level changes in adult ICU bed supply in the United States. Crit Care Med 2017;45:e67–e76.CrossRefGoogle ScholarPubMed
Al Mohajer, M, Joiner, KA, Nix, DE. Are teaching hospitals treated fairly in the hospital-acquired condition reduction program? Acad Med 2018;93:1827–1832.CrossRefGoogle ScholarPubMed
Clostridium difficile infections (CDI) in Healthcare. California Department of Public Health (CDPH), California Health and Human Services website. https://data.chhs.ca.gov/dataset/clostridium-difficile-infections-cdi-in-healthcare. Accessed August 4, 2019.Google Scholar
California Department of Public Health (CDPH). Healthcare-associated infections in California hospitals annual report, 2015. California Health and Human Services website https://www.cdph.ca.gov/Programs/CHCQ/HAI/CDPH%20Document%20Library/HAIinCAHospitalsAnnualReport2015_091216ADA_EDITEDopa041417.pdf. Published 2016. Accessed August 4, 2019.Google Scholar
Vokes, RA, Bearman, G, Bazzoli, GJ. Hospital-acquired infections under pay-for-performance systems: an administrative perspective on management and change. Curr Infect Dis Rep 2018;20:35.CrossRefGoogle ScholarPubMed
Your hospital’s VBP penalty or bonus for 2018, mapped. Advisory Board website https://www.advisory.com/daily-briefing/2017/11/08/value-based-payments-18. Published 2017. Accessed August 4, 2017.Google Scholar
CMS hospital value-based purchasing program results for fiscal year 2018. Centers for Medicare and Medicaid Services website. https://www.cms.gov/newsroom/fact-sheets/cms-hospital-value-based-purchasing-program-results-fiscal-year-2018. Published 2018. Accessed August 4, 2019.Google Scholar
Burke, LG, Frakt, AB, Khullar, D, Orav, EJ, Jha, AK. Association between teaching status and mortality in US hospitals. JAMA 2017;317:2105–2113.CrossRefGoogle ScholarPubMed
Shahian, DM, Nordberg, P, Meyer, GS, et al.Contemporary performance of US teaching and nonteaching hospitals. Acad Med 2012;87:701–708.CrossRefGoogle Scholar
Thompson, ND, Edwards, JR, Dudeck, MA, Fridkin, SK, Magill, SS. Evaluating the use of the case mix index for risk adjustment of healthcare-associated infection data: an illustration using Clostridium difficile infection data from the National Healthcare Safety Network. Infect Control Hosp Epidemiol 2016;37:19–25.CrossRefGoogle ScholarPubMed
Revolinski, SL, Munoz-Price, LS. Clostridium difficile in immunocompromised hosts: a review of epidemiology, risk factors, treatment, and prevention. Clin Infect Dis 2019;68:2144–2153.CrossRefGoogle ScholarPubMed
National Healthcare Safety Network (NHSN). Rebaseline timeline FAQ. Centers for Disease Control and Prevention website. https://www.cdc.gov/nhsn/pdfs/rebaseline/faq-timeline-rebaseline.pdf. Accessed December 14, 2019.Google Scholar
Centers for Medicare & Medicaid Services (CMS) and QualityNet. Hospital Value-Based Purchasing (HVBP) Program FY 2019 resources; FY 2019 value-based purchasing domain weighting. Centers for Medicare & Medicaid Services (CMS) website. https://www.qualitynet.org/files/5d0d398e764be766b0103923?filename=FY19_VBP_Updtd_DmnWghtg_011118.pdf. Published 2019. Accessed December 14, 2019.Google Scholar
Medicare Learning Network (MLN). Hospital value-based purchasing MLN booklet. Centers for Medicare & Medicaid Services (CMS) website. https://www.cms.gov/Outreach-and-Education/Medicare-Learning-Network-MLN/MLNProducts/downloads/Hospital_VBPurchasing_Fact_Sheet_ICN907664.pdf. Accessed December 14, 2019.Google Scholar
Albert, K, Ross, B, Calfee, DP, Simon, MS. Overreporting healthcare-associated C. difficile: a comparison of NHSN LabID with clinical surveillance definitions in the era of molecular testing. Am J Infect Control 2018;46:998–1002.CrossRefGoogle ScholarPubMed
Durkin, MJ, Baker, AW, Dicks, KV, et al.A comparison between National Healthcare Safety Network laboratory-identified event reporting versus traditional surveillance for Clostridium difficile infection. Infect Control Hosp Epidemiol 2015;36:125–131.CrossRefGoogle ScholarPubMed
Gase, KA, Haley, VB, Xiong, K, Van Antwerpen, C, Stricof, RL. Comparison of two Clostridium difficile surveillance methods: National Healthcare Safety Network’s laboratory-identified event reporting module versus clinical infection surveillance. Infect Control Hosp Epidemiol 2013;34:284–290.CrossRefGoogle Scholar
Marra, AR, Edmond, MB, Ford, BA, Herwaldt, LA, Algwizani, AR, Diekema, DJ. Impact of 2018 changes in National Healthcare Safety Network surveillance for Clostridium difficile laboratory-identified event reporting. Infect Control Hosp Epidemiol 2018;39:886–888.CrossRefGoogle ScholarPubMed
State Inpatient Database (SID) database documentation. Healthcare Cost and Utilization Project (H-CUP) website. https://www.hcup-us.ahrq.gov/db/state/siddbdocumentation.jsp. Accessed August 4, 2019.Google Scholar
Loo, VG, Bourgault, AM, Poirier, L, et al.Host and pathogen factors for Clostridium difficile infection and colonization. N Engl J Med 2011;365:1693–1703.CrossRefGoogle ScholarPubMed
Miller, AC, Polgreen, LA, Cavanaugh, JE, Polgreen, PM. Hospital Clostridium difficile infection rates and prediction of length of stay in patients without C. difficile infection. Infect Control Hosp Epidemiol 2016;37:404–410.CrossRefGoogle ScholarPubMed
Supplementary material: PDF

Polage et al. supplementary material

Polage et al. supplementary material

Download Polage et al. supplementary material(PDF)
PDF 115.2 KB