Infection Control & Hospital Epidemiology最新文献

{"title":"Frequency of Use of Alcohol-Based Hand Rubs by Nurses: A Systematic Review.","authors":"John M Boyce, Philip M Polgreen, Mauricio Monsalve, David R Macinga, James W Arbogast","doi":"10.1017/ice.2016.247","DOIUrl":"10.1017/ice.2016.247","url":null,"abstract":"<p><p>BACKGROUND Recently, the US Food and Drug Administration requested that a \"maximal use\" trial be conducted to ensure the safety of frequent use of alcohol-based hand rubs (ABHRs) by healthcare workers. OBJECTIVE To establish how frequently volunteers should be exposed to ABHR during a maximal use trial. DESIGN Retrospective review of literature and analysis of 2 recent studies that utilized hand hygiene electronic compliance monitoring (ECM) systems. METHODS We reviewed PubMed for articles published between 1970 and December 31, 2015, containing the terms hand washing, hand hygiene, hand hygiene compliance, and alcohol-based hand rubs. Article titles, abstracts, or text were reviewed to determine whether the frequency of ABHR use by healthcare workers was reported. Two studies using hand hygiene ECM systems were reviewed to determine how frequently nurses used ABHR per shift and per hour. RESULTS Of 3,487 citations reviewed, only 10 reported how frequently individual healthcare workers used ABHR per shift or per hour. Very conservative estimates of the frequency of ABHR use were reported owing to shortcomings of the methods utilized. The greatest frequency of ABHR use was recorded by an ECM system in a medical intensive care unit. In 95% of nursing shifts, individual nurses used ABHR 141 times or less per shift, and 15 times or less per hour. CONCLUSIONS Hand hygiene ECM systems established that the frequency of exposure to ABHRs varies substantially among nurses. Our findings should be useful in designing how frequently individuals should be exposed to ABHR during a maximal use trial. Infect Control Hosp Epidemiol 2017;38:189-195.</p>","PeriodicalId":13655,"journal":{"name":"Infection Control &#x0026; Hospital Epidemiology","volume":"13 1","pages":"189-195"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75846345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the Impact of Interventions to Prevent Antimicrobial Resistant Infections in the Long-Term Care Facility: A Review and Practical Guide to Mathematical Modeling.","authors":"Alicia Rosello, Carolyne Horner, Susan Hopkins, Andrew C Hayward, Sarah R Deeny","doi":"10.1017/ice.2016.286","DOIUrl":"10.1017/ice.2016.286","url":null,"abstract":"<p><p>OBJECTIVES (1) To systematically search for all dynamic mathematical models of infectious disease transmission in long-term care facilities (LTCFs); (2) to critically evaluate models of interventions against antimicrobial resistance (AMR) in this setting; and (3) to develop a checklist for hospital epidemiologists and policy makers by which to distinguish good quality models of AMR in LTCFs. METHODS The CINAHL, EMBASE, Global Health, MEDLINE, and Scopus databases were systematically searched for studies of dynamic mathematical models set in LTCFs. Models of interventions targeting methicillin-resistant Staphylococcus aureus in LTCFs were critically assessed. Using this analysis, we developed a checklist for good quality mathematical models of AMR in LTCFs. RESULTS AND DISCUSSION Overall, 18 papers described mathematical models that characterized the spread of infectious diseases in LTCFs, but no models of AMR in gram-negative bacteria in this setting were described. Future models of AMR in LTCFs require a more robust methodology (ie, formal model fitting to data and validation), greater transparency regarding model assumptions, setting-specific data, realistic and current setting-specific parameters, and inclusion of movement dynamics between LTCFs and hospitals. CONCLUSIONS Mathematical models of AMR in gram-negative bacteria in the LTCF setting, where these bacteria are increasingly becoming prevalent, are needed to help guide infection prevention and control. Improvements are required to develop outputs of sufficient quality to help guide interventions and policy in the future. We suggest a checklist of criteria to be used as a practical guide to determine whether a model is robust enough to test policy. Infect Control Hosp Epidemiol 2017;38:216-225.</p>","PeriodicalId":13655,"journal":{"name":"Infection Control &#x0026; Hospital Epidemiology","volume":"1 1","pages":"216-225"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77291986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incidence and Outcomes Associated With Infections Caused by Vancomycin-Resistant Enterococci in the United States: Systematic Literature Review and Meta-Analysis.","authors":"Hsiu-Yin Chiang, Eli N Perencevich, Rajeshwari Nair, Richard E Nelson, Matthew Samore, Karim Khader, Margaret L Chorazy, Loreen A Herwaldt, Amy Blevins, Melissa A Ward, Marin L Schweizer","doi":"10.1017/ice.2016.254","DOIUrl":"10.1017/ice.2016.254","url":null,"abstract":"<p><p>BACKGROUND Information about the health and economic impact of infections caused by vancomycin-resistant enterococci (VRE) can inform investments in infection prevention and development of novel therapeutics. OBJECTIVE To systematically review the incidence of VRE infection in the United States and the clinical and economic outcomes. METHODS We searched various databases for US studies published from January 1, 2000, through June 8, 2015, that evaluated incidence, mortality, length of stay, discharge to a long-term care facility, readmission, recurrence, or costs attributable to VRE infections. We included multicenter studies that evaluated incidence and single-center and multicenter studies that evaluated outcomes. We kept studies that did not have a denominator or uninfected controls only if they assessed postinfection length of stay, costs, or recurrence. We performed meta-analysis to pool the mortality data. RESULTS Five studies provided incidence data and 13 studies evaluated outcomes or costs. The incidence of VRE infections increased in Atlanta and Detroit but did not increase in national samples. Compared with uninfected controls, VRE infection was associated with increased mortality (pooled odds ratio, 2.55), longer length of stay (3-4.6 days longer or 1.4 times longer), increased risk of discharge to a long-term care facility (2.8- to 6.5-fold) or readmission (2.9-fold), and higher costs ($9,949 higher or 1.6-fold more). CONCLUSIONS VRE infection is associated with large attributable burdens, including excess mortality, prolonged in-hospital stay, and increased treatment costs. Multicenter studies that use suitable controls and adjust for time at risk or confounders are needed to estimate the burden of VRE infections. Infect Control Hosp Epidemiol. 2017;38:203-215.</p>","PeriodicalId":13655,"journal":{"name":"Infection Control &#x0026; Hospital Epidemiology","volume":"21 1","pages":"203-215"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84474192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prolonged Rhinovirus Shedding in a Patient with Hodgkin Disease","authors":"Andrew T. Dysangco, A. Kressel, Stephanie Dearth, Reema Patel, Shawn M. Richards","doi":"10.1017/ice.2016.338","DOIUrl":"https://doi.org/10.1017/ice.2016.338","url":null,"abstract":"To the Editor—Respiratory viral pathogens (RVPs) have been increasingly identified as a serious concern in immunocompromised patients. In this population, RVPs cause more lower-respiratory tract infections (LRIs), leading to increased mortality and morbidity. Prolonged viral shedding of RVP can become an infection control problem and has been implicated in at least 1 hospital outbreak. With respect to the hematopoietic stem cell transplant (HSCT) population, most publications have studied more virulent RVPs, whereas data on the nontransplant immunocompromised population with less virulent RVP are lacking altogether. Compared with other RVPs, rhinoviruses (RVs) cause proportionately fewer LRIs in the healthy population, but RVs are more prevalent than other RVPs and infect 22.3% of HSCT recipients within 100 days of transplantation. In a small retrospective study of immunocompromised patients and without inferring causation, RVs were associated with the same mortality as the 2009 H1N1 influenza. We report a patient with relapsed Hodgkin’s Disease (HD) without a transplant who was found to have prolonged RV shedding of 96 days with LRI. Our patient was a 37-year-old man with prior lung injury from acute respiratory distress syndrome, CD4 lymphopenia with recurrent pneumonia, and relapsed HD after treatment with bleomycin, adriamycin, vinblastine, and dacarbazine, treated with brentuximab. He experienced intermittent fever beginning in September 2014 and presented in late October 2014 with progressive dyspnea, continuing intermittent fever, and a nonproductive cough. He was hypoxemic on admission. Chest CT showed bilateral ground-glass opacities. Bronchoalveolar lavage (BAL) performed on October 29, 2014, was RT-PCR positive for RV/ enterovirus (EV). Other infectious disease testing was negative. Intravenous immunoglobulin was given with tapering prednisone for bronchospasm. He improved and was discharged a few days later. He remained afebrile with continued dry cough and dyspnea during November and December. In January, he began having afternoon fevers (38.9–39.5°C [102–103°F]), dyspnea, productive cough of whitish to yellow sputum, weight loss, drenching night sweats, and lymphadenopathy. He was readmitted in late January 2015 with severe sepsis and hypoxemia. Another chest CT showed progression of interstitial and airspace opacities. A nasopharyngeal swab was collected on January 31, 2015, and BAL was performed on February 2, 2015; both were RT-PCR positive for RV/EV; adenovirus PCR was also positive on the BAL. The patient was transitioned to comfort care after a repeat biopsy showed progression of HD, and he died February 5, 2015. Sanger-sequencing and bioinformatic analyses of clinical specimens from October 29, 2014, January 31, 2015, and February 2, 2015, identified RV-A51. Prolonged viral shedding, seen in immunocompromised patients, is dependent on the host’s immune status, virus species and strain, lung injury, and other risk factors, all","PeriodicalId":13655,"journal":{"name":"Infection Control &#x0026; Hospital Epidemiology","volume":"369 1","pages":"500 - 501"},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85459635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Catheter-Associated Urinary Tract Infection: Utility of the ICD-10 Metric as a Surrogate for the National Healthcare Safety Network (NHSN) Surveillance Metric","authors":"A. Marra, Mufareh Alkatheri, M. Edmond","doi":"10.1017/ice.2016.335","DOIUrl":"https://doi.org/10.1017/ice.2016.335","url":null,"abstract":"Affiliations: 1. Healthcare Infection Prevention Program, Virginia Commonwealth University Health System, Richmond, Virginia; 2. Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa. Address correspondence to Nadia Masroor, BS, VCU Health System, Box 980019, Richmond, Virginia 23298 (nadia.masroor@vcuhealth.org). Infect Control Hosp Epidemiol 2017;38:504–506 © 2017 by The Society for Healthcare Epidemiology of America. All rights reserved. 0899-823X/2017/3804-0026. DOI: 10.1017/ice.2016.337","PeriodicalId":13655,"journal":{"name":"Infection Control &#x0026; Hospital Epidemiology","volume":"7 1","pages":"506 - 507"},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90859658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interhospital Comparison of Surgical Site Infection Rates in Orthopedic Surgery","authors":"J. Skufca, J. Ollgren, M. Virtanen, K. Huotari, O. Lyytikäinen","doi":"10.1017/ice.2016.333","DOIUrl":"https://doi.org/10.1017/ice.2016.333","url":null,"abstract":"OBJECTIVE To investigate whether comparison by deep or adjusted deep surgical site infection (SSI) rates in orthopedic surgeries are a better basis for feedback to Finnish hospitals than overall SSI rates DESIGN Retrospective cohort study SETTING Hospitals conducting surveillance of hip arthroplasties (HPROs) and knee arthroplasties (KPROs) in the Finnish Hospital Infection Program METHODS We analyzed surveillance data for 73,227 HPROs and 56,860 KPROs performed in 18 hospitals during 1999–2014. For each hospital, the overall, deep, and adjusted deep SSI rates with 95% confidence intervals (CIs) were calculated, and the hospital ranks were simulated in the Bayesian framework. Adjustments were performed using relevant patient and hospital characteristics. The correlation between the median expected hospital ranks in overall versus deep SSI rates and deep vs adjusted deep SSI rates were assessed using Spearman’s correlation coefficient ρ. RESULTS For HPRO, the overall SSI rates ranged from 0.92 to 6.83, the deep SSI rates ranged from 0.34 to 1.86, and the adjusted deep hospital-specific SSI rates ranged from 0.37 to 1.85. For KPRO, the overall SSI rates ranged from 0.71 to 5.03, the deep SSI rates ranged from 0.42 to 1.60, and the adjusted deep hospital-specific SSI rates ranged from 0.56 to 1.55. For both procedures, the 95% CIs of the rates between hospitals largely overlapped; only single outliers were detected. Hospital rank did not correlate between overall and deep SSI rates (HPRO, ρ=0.03; KPRO, ρ=0.40), but a correlation was observed in hospital rank for deep and adjusted deep SSI rates (HPRO, ρ=0.85; KPRO, ρ=0.94). CONCLUSION Deep SSI rates may be a better tool for interhospital comparisons than overall SSI rates. Although the adjustment could lead to fairer hospital ranking, it is not always necessary for feedback. Infect Control Hosp Epidemiol 2017;38:423–429","PeriodicalId":13655,"journal":{"name":"Infection Control &#x0026; Hospital Epidemiology","volume":"99 1","pages":"423 - 429"},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79297794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reply to Weber, von Cube, Sommer, Wolkewitz: Necessity of a Competing Risk Approach in Risk Factor Analysis of Central-Line–Associated Bloodstream Infection","authors":"S. Kuhle, J. Carter, S. Kirkland, J. Langley, B. Maguire, Bruce Smith","doi":"10.1017/ice.2016.331","DOIUrl":"https://doi.org/10.1017/ice.2016.331","url":null,"abstract":"To the Editor—We thank Ms. Weber and colleagues for their comments regarding the use of the Cox proportional hazards model to analyze risk factors for central-line–associated bloodstream infections (CLABSIs) in children, in which we used a Cox proportional hazards model to determine risk factors for this outcome. In our analysis, removal of the central venous cathether was treated as censoring. Weber et al suggest that removal of the line constitutes a competing risk for CLABSI because children without a line can no longer be assumed to be at the same risk for CLABSI than those with a line (the fundamental assumption of censoring). We sincerely appreciate these comments, which highlight the need for increased awareness of the assumptions of the Cox proportional hazard method in this setting. We agree that removal of the central venous catheter indeed constitutes a competing risk. In our cohort study, there were only 2 possible outcomes with regard to the life of the central venous catheter: infection and catheter removal. Because all lines are followed by the infection control team until removal, there was no censoring due to loss to follow-up. We have re-analyzed the data and have graphed the cumulative incidence function of CLABSI as suggested by Weber et al. The curve reaches the empirical cumulative incidence of CLABSI of 6.8% on the day of the last event (Figure 1). We have further rerun the Cox proportional hazards model using (1) the subdistribution hazard (SHR) approach and (2) the cause-specific hazard approach (modeling the time to line infection or catheter removal separately, each time treating the other as the censoring event). After reviewing the literature and in discussion with statistician colleagues, we feel that the first approach (SHR) is not suitable to answer our research question. The SHR approach describes the CLABSI risk in patients who already had their line removed (the competing event), ie, in a non-existing, theoretical population. This approach has been advocated in the literature for prediction modeling rather than etiologic research (like our study). By contrast, the hazard ratios from the cause-specific models can be interpreted as the risk of CLABSI in patients who have not (yet) had CLABSI and have not had their catheter removed (the competing event). Within this interpretation of the hazard ratios, the estimates presented in our paper are correct.","PeriodicalId":13655,"journal":{"name":"Infection Control &#x0026; Hospital Epidemiology","volume":"4 1","pages":"511 - 511"},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79905040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Urine Culture Testing in Community Nursing Homes: Gateway to Antibiotic Overprescribing","authors":"P. Sloane, C. Kistler, D. Reed, D. Weber, Kimberly T Ward, S. Zimmerman","doi":"10.1017/ice.2016.326","DOIUrl":"https://doi.org/10.1017/ice.2016.326","url":null,"abstract":"OBJECTIVE To describe current practice around urine testing and identify factors leading to overtreatment of asymptomatic bacteriuria in community nursing homes (NHs) DESIGN Observational study of a stratified random sample of NH patients who had urine cultures ordered in NHs within a 1-month study period SETTING 31 NHs in North Carolina PARTICIPANTS 254 NH residents who had a urine culture ordered within the 1-month study period METHODS We conducted an NH record audit of clinical and laboratory information during the 2 days before and 7 days after a urine culture was ordered. We compared these results with the urine antibiogram from the 31 NHs. RESULTS Empirical treatment was started in 30% of cases. When cultures were reported, previously untreated cases received antibiotics 89% of the time for colony counts of ≥100,000 CFU/mL and in 35% of cases with colony counts of 10,000–99,000 CFU/mL. Due to the high rate of prescribing when culture results returned, 74% of these patients ultimately received a full course of antibiotics. Treated and untreated patients did not significantly differ in temperature, frequency of urinary signs and symptoms, or presence of Loeb criteria for antibiotic initiation. Factors most commonly associated with urine culture ordering were acute mental status changes (32%); change in the urine color, odor, or sediment (17%); and dysuria (15%). CONCLUSIONS Urine cultures play a significant role in antibiotic overprescribing. Antibiotic stewardship efforts in NHs should include reduction in culture ordering for factors not associated with infection-related morbidity as well as more scrutiny of patient condition when results become available. Infect Control Hosp Epidemiol 2017;38:524–531","PeriodicalId":13655,"journal":{"name":"Infection Control &#x0026; Hospital Epidemiology","volume":"24 1","pages":"524 - 531"},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86567534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenging Residual Contamination of Instruments for Robotic Surgery in Japan","authors":"N. von Landenberg, A. Cole, P. Gild, Q. Trinh","doi":"10.1017/ice.2016.334","DOIUrl":"https://doi.org/10.1017/ice.2016.334","url":null,"abstract":"Affiliations: 1. Indiana University School of Medicine, Indianapolis, Indiana; 2. Indiana State Department of Health, Indianapolis, Indiana. Address correspondence to Andrew T. Dysangco, MD, 545 Barnhill Dr. Emerson Hall, Suite 421, Indianapolis, IN 46202-5124 (andysang@iupui. edu). PREVIOUS PRESENTATION: These findings were presented as a poster in the SHEA Spring Conference, May 19, 2016, in Atlanta, Georgia.","PeriodicalId":13655,"journal":{"name":"Infection Control &#x0026; Hospital Epidemiology","volume":" 2","pages":"501 - 502"},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91411803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measures to Prevent and Control Vancomycin-Resistant Enterococci: Do They Really Matter?","authors":"H. Humphreys","doi":"10.1017/ice.2016.329","DOIUrl":"https://doi.org/10.1017/ice.2016.329","url":null,"abstract":"present. Contaminated urine cultures (≥3 organisms present) were misclassified as infections in 6 of 58 cases (10.3%), and in 5 of 58 cases (8.6%), no urine culture was obtained. Lastly, in 15 of 58 cases (25.9%), bacteriuria was present (1 or 2 organisms), but the colony count did not reach the NHSN metric threshold of ≥ 100,000 CFU/mL. The study period comprised 233,921 patient days. The CAUTI rate was 0.24 CAUTIs per 1,000 patient days using the ICD-10-CM metric; this rate was 0.18 when POA cases were eliminated. The CAUTI rate was 0.20 per 1,000 patient days using the NHSN metric. The NHSN CAUTI metric and the ICD-10-CM CAUTI-like code produce widely discrepant results. Even when ICD-10 cases that were POA were removed to better align with the NHSN criteria, the sensitivity of the ICD-10 metric was only 2.4%. Importantly, no patient safety indicator from AHRQ is available for CAUTI as there is for central venous catheterrelated bloodstream infection. This was the primary reason that we used the administrative code (ICD-10-CM) to compare to NHSN surveillance data for detecting CAUTI. Our results demonstrate that updating ICD-9-CMwith more codes to produce ICD-10-CM did not improve the ability of administrative data to identify CAUTIs. The date of the event is an important element used to meet an NHSN site-specific infection criterion, including CAUTI, and that is one reason that administrative data fail to accurately identify cases of HAI. This study has several limitations. First, it was performed in a single medical center. In addition, we did not review the negative cases via either method, and we assumed that traditional surveillance (NHSN) is the gold standard surveillance method. Therefore, it was not possible to calculate the specificity because our aim was to compare only NHSN and ICD-10-CM CAUTI identified cases. Given that CAUTI is a relatively rare event, we can assume that the specificity of the ICD-10-CM metric is high. In summary, we found that ICD-10-CM has an extremely low sensitivity for detecting CAUTI cases; it failed to detect 98.3% of the infections at our institution. Almost all cases identified via ICD-10-CM did not fulfill the NHSN criteria. Thus, administrative coding for this HAI is not a useful tool for use as a surveillance method.","PeriodicalId":13655,"journal":{"name":"Infection Control &#x0026; Hospital Epidemiology","volume":"12 1","pages":"507 - 509"},"PeriodicalIF":0.0,"publicationDate":"2017-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80753664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}