Infection Control and Hospital Epidemiology最新文献

{"title":"Changes in use of multiplex respiratory panel testing during the COVID-19 pandemic.","authors":"Jonathan D Baghdadi, Chih Chun Tung, J Kristie Johnson, Daniel J Morgan, Anthony D Harris","doi":"10.1017/ice.2024.163","DOIUrl":"https://doi.org/10.1017/ice.2024.163","url":null,"abstract":"<p><strong>Background: </strong>COVID-19 changed the epidemiology of community-acquired respiratory viruses. We explored patterns of respiratory viral testing to understand which tests are most clinically useful in the postpandemic era.</p><p><strong>Methods: </strong>We conducted a retrospective observational study of discharge data from PINC-AI (formerly Premier), a large administrative database. Use of multiplex nucleic acid amplification respiratory panels in acute care, including small (2-5 targets), medium (6-11), and large panels (>11), were compared between the early pandemic (03/2020-10/2020), late pandemic (11/2020-4/2021), and prepandemic respiratory season (11/2019 - 02/2020) using ANOVA.</p><p><strong>Results: </strong>A median of 160.5 facilities contributed testing data per quarter (IQR 155.5-169.5). Prepandemic, facilities averaged 103 respiratory panels monthly (sd 138), including 79 large (sd 126), 7 medium (sd 31), and 16 small panels (sd 73). Relative to prepandemic, utilization decreased during the early pandemic (62 panels monthly/facility; sd 112) but returned to the prepandemic baseline by the late pandemic (107 panels monthly/facility; sd 211). Relative to prepandemic, late pandemic testing involved more small panel use (58 monthly/facility, sd 156) and less large panel use (47 monthly/facility, sd 116). Comparisons among periods demonstrated significant differences in overall testing (<i>P</i> < 0.0001), large panel use (<i>P</i> < 0.0001), and small panel use (<i>P</i> < 0.0001).</p><p><strong>Conclusions: </strong>Postpandemic, clinical use of respiratory panel testing shifted from predominantly large panels to predominantly small panels. Factors driving this change may include resource availability, costs, and the clinical utility of targeting important pathogenic viruses instead of testing \"for everything.\"</p>","PeriodicalId":13663,"journal":{"name":"Infection Control and Hospital Epidemiology","volume":" ","pages":"1-6"},"PeriodicalIF":3.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging real-time patient data during the COVID-19 pandemic.","authors":"John C O'Horo, Douglas W Challener, Cory Kudrna, Jason R Buckmeier, Steve G Peters, Daryl J Kor, Mark W Matson, Andrew D Badley, Charles D Burger, Rajeev Chaudhry","doi":"10.1017/ice.2024.118","DOIUrl":"https://doi.org/10.1017/ice.2024.118","url":null,"abstract":"","PeriodicalId":13663,"journal":{"name":"Infection Control and Hospital Epidemiology","volume":" ","pages":"1-2"},"PeriodicalIF":3.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Healthcare-associated infections in Japanese hospitals: results from a large-scale multicenter point-prevalence survey in Aichi, 2020.","authors":"Hiroshi Morioka, Yusuke Koizumi, Keisuke Oka, Masami Okudaira, Yuka Tomita, Yumi Kojima, Toshitaka Watariguchi, Koichi Watamoto, Yoshikazu Mutoh, Takeshi Tsuji, Manabu Yokota, Junichi Shimizu, Chihiro Hasegawa, Susumu Iwata, Masatoshi Nagaoka, Yuji Ito, Shohei Kawasaki, Hiroki Kato, Yuichi Kitagawa, Takuya Goto, Yasuhiro Nozaki, Kenji Akita, Shinsuke Shimizu, Masanori Nozawa, Munehiro Kato, Masamitsu Ishihara, Kenta Ito, Tetsuya Yagi","doi":"10.1017/ice.2024.130","DOIUrl":"https://doi.org/10.1017/ice.2024.130","url":null,"abstract":"<p><strong>Objective: </strong>Healthcare-associated infections (HAIs) pose significant challenges to healthcare systems worldwide. Epidemiological data are essential for effective HAI control; however, comprehensive information on HAIs in Japanese hospitals is limited. This study aimed to provide an overview of HAIs in Japanese hospitals.</p><p><strong>Methods: </strong>A multicenter point-prevalence survey (PPS) was conducted in 27 hospitals across the Aichi Prefecture between February and July 2020. This study encompassed diverse hospital types, including community, university, and specialized hospitals. Information on the demographic data of the patients, underlying conditions, devices, HAIs, and causative organisms was collected.</p><p><strong>Results: </strong>A total of 10,199 patients (male: 5,460) were included in this study. The median age of the patients was 73 (interquartile range [IQR]: 56-82) years, and the median length of hospital stay was 10 (IQR: 4-22) days. HAIs were present in 6.6% of patients, with pneumonia (1.83%), urinary tract infection (1.09%), and surgical site infection (SSI) (0.87%) being the most common. The prevalence of device-associated HAIs was 0.91%. <i>Staphylococcus aureus</i> (17.3%), <i>Escherichia coli</i> (17.1%), and <i>Klebsiella pneumoniae</i> (7.2%) were the primary pathogens in 433 organisms; 29.6% of the <i>Enterobacterales</i> identified showed resistance to third-generation cephalosporins. Pneumonia was the most prevalent HAI in small-to-large hospitals (1.69%-2.34%) and SSI, in extra-large hospitals (over 800 beds, 1.37%).</p><p><strong>Conclusions: </strong>This study offers vital insights into the epidemiology of HAIs in hospitals in Japan. These findings underscore the need for national-level PPSs to capture broader epidemiological trends, particularly regarding healthcare challenges post-COVID-19.</p>","PeriodicalId":13663,"journal":{"name":"Infection Control and Hospital Epidemiology","volume":" ","pages":"1-8"},"PeriodicalIF":3.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Importance of risk adjusting central line-associated bloodstream infection rates in children.","authors":"Lakshmi Srinivasan, Ashley Oliver, Yuan-Shung V Huang, Di Shu, Kait M Donnelly, Cecelia Harrison, Amy L Roberts, Ron Keren","doi":"10.1017/ice.2024.111","DOIUrl":"https://doi.org/10.1017/ice.2024.111","url":null,"abstract":"<p><strong>Objective: </strong>Central line-associated bloodstream infection (CLABSI) is one of the most prevalent pediatric healthcare-associated infections and is used to benchmark hospital performance. Pediatric patients have increased in acuity and complexity over time. Existing approaches to risk adjustment do not control for individual patient characteristics, which are strong predictors of CLABSI risk and vary over time. Our objective was to develop a risk adjustment model for CLABSI in hospitalized children and compare observed to expected rates over time.</p><p><strong>Design and setting: </strong>We conducted a prospective cohort study using electronic health record data at a quaternary Children's Hospital.</p><p><strong>Patients: </strong>We included hospitalized children with central catheters.</p><p><strong>Methods: </strong>Risk factors identified from published literature were considered for inclusion in multivariable modeling based on association with CLABSI risk in bivariable analysis and expert input. We calculated observed and expected (risk model-adjusted) annual CLABSI rates.</p><p><strong>Results: </strong>Among 16,411 patients with 520,209 line days, 633 patients experienced 796 CLABSIs. The final model included age, behavioral health condition, non-English speaking, oncology service, port catheter type, catheter dwell time, lymphatic condition, total parenteral nutrition, and number of organ systems requiring ICU level care. For every organ system receiving ICU level care the odds ratio for CLABSI was 1.24 (95% CI 1.12-1.37). Although not statistically different, observed rates were lower than expected rates for later years.</p><p><strong>Conclusions: </strong>Failure to adjust for patient factors, particularly acuity and complexity of disease, may miss clinically significant differences in CLABSI rates, and may lead to inaccurate interpretation of the impact of quality improvement efforts.</p>","PeriodicalId":13663,"journal":{"name":"Infection Control and Hospital Epidemiology","volume":" ","pages":"1-6"},"PeriodicalIF":3.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142380670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Postoperative outcomes after receipt of ertapenem antimicrobial prophylaxis for colon surgery: a multicenter retrospective cohort study.","authors":"Christopher J Hostler, Jay Krishnan, Alice Parish, Allison Baroco, Penny Cooper, Onofre Donceras, Ebbing Lautenbach, Pam Tolomeo, Tracy Sansossio, Carlos A Q Santos, David Schwartz, Helen Zhang, Sharon Welbel, Yuliya Lokhnygina, Deverick J Anderson","doi":"10.1017/ice.2024.99","DOIUrl":"https://doi.org/10.1017/ice.2024.99","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate postoperative outcomes among patients undergoing colon surgery who receive perioperative prophylaxis with ertapenem compared to other antibiotic regimens.</p><p><strong>Design and setting: </strong>Multicenter retrospective cohort study among adults undergoing colon surgery in seven hospitals across three health systems from 1/1/2010 to 9/1/2015.</p><p><strong>Methods: </strong>Generalized linear mixed logistic regression models were applied to assess differential odds of select outcomes among patients who received perioperative prophylaxis with ertapenem compared to other regimens. Postoperative outcomes of interest included surgical site infection (SSI), <i>Clostridioides difficile</i> infection (CDI) and clinical culture positivity for carbapenem-resistant <i>Enterobacteraciae</i> (CRE). Inverse probability weights were applied to account for differing covariate distributions across ertapenem and non-ertapenem groups.</p><p><strong>Results: </strong>A total of 2,109 patients were included for analysis. The odds of postoperative SSI was 1.56 times higher among individuals who received ertapenem than among those receiving other perioperative antimicrobial prophylaxis regimens in our cohort (46 [3.5%] vs 20 [2.5%]; IPW-weighted OR 1.56, [95% CI, 1.08-2.26], <i>P</i> = .02). No statistically significant differences in odds of postoperative CDI (24 [1.8%] vs 16 [2.0%]; IPW-weighted OR 1.07 [95% CI, .68-1.68], <i>P</i> = .78) were observed between patients who received ertapenem prophylaxis compared to other regimens. Clinical CRE culture positivity was rare in both groups (.2%-.5%) and did not differ statistically.</p><p><strong>Conclusions: </strong>Ertapenem use for perioperative prophylaxis was associated with increased odds of SSI among patients undergoing colon surgery in our study population, though no differences in CDI or clinical CRE culture positivity were identified. Further study and replication of these findings are needed.</p>","PeriodicalId":13663,"journal":{"name":"Infection Control and Hospital Epidemiology","volume":" ","pages":"1-6"},"PeriodicalIF":3.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142371756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The burden of healthcare-associated infections in New Zealand public hospitals 2021.","authors":"Arthur J Morris, Mike Hensen, Nicholas Graves, Yiying Cai, Martin Wolkewitz, Sally A Roberts, Nikki Grae","doi":"10.1017/ice.2024.95","DOIUrl":"https://doi.org/10.1017/ice.2024.95","url":null,"abstract":"<p><strong>Background: </strong>There are no contemporary data on the burden of healthcare-associated infections (HAIs) in New Zealand.</p><p><strong>Objectives: </strong>To estimate the economic burden of HAIs in adults in New Zealand public hospitals by number and monetary value of bed days lost; number of deaths, number of life years lost, and the monetary value (in NZ dollars); Accident Compensation Commission (ACC) HAI treatment injury payments; and disability-adjusted life years (DALYs).</p><p><strong>Methods: </strong>The annual incidence rate was calculated from the observed prevalence of HAIs in New Zealand, and length of patient stays. Total HAIs for 2021 were estimated by multiplying adult admissions by incidence rates. The excess length of stay and mortality risk attributed to those with HAI was calculated using a multistate model. Payments for treatment injuries were obtained from the ACC. DALYs for HAIs were estimated from the literature.</p><p><strong>Results: </strong>The incidence rate of HAI was 4.74%, predicting 24,191 HAIs for 2021, resulting in 76,861 lost bed days, 699 deaths, with 9,371 years of life lost (YoLL). The annual economic burden was estimated to be $955m comprised of $121m for lost bed days, $792m for cost of YoLL, and $43m ACC claims. There were 24,165 DALY which is greater than many other measured injuries in New Zealand, eg motor vehicle traffic crashes with 20,328 DALY.</p><p><strong>Conclusions: </strong>HAIs are a significant burden for patients, their families, and the public health system. Preventive guidelines for many HAIs exist and a strategic plan is needed to reduce HAIs in New Zealand.</p>","PeriodicalId":13663,"journal":{"name":"Infection Control and Hospital Epidemiology","volume":" ","pages":"1-7"},"PeriodicalIF":3.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142371758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness and feasibility of a penicillin allergy delabeling program in the postacute inpatient rehabilitation setting.","authors":"Joseph Galipean, Jerry Jacob","doi":"10.1017/ice.2024.138","DOIUrl":"https://doi.org/10.1017/ice.2024.138","url":null,"abstract":"","PeriodicalId":13663,"journal":{"name":"Infection Control and Hospital Epidemiology","volume":" ","pages":"1-3"},"PeriodicalIF":3.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142371754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk of SARS-CoV-2 infection in hospitalized patients following SARS-CoV-2 exposures before and during hospitalization.","authors":"Selina Ehrenzeller, Anna Agan, Chanu Rhee, Michael Klompas","doi":"10.1017/ice.2024.136","DOIUrl":"https://doi.org/10.1017/ice.2024.136","url":null,"abstract":"<p><p>Little is known regarding SARS-CoV-2 infection risk following SARS-CoV-2 exposures in hospitalized patients. Amongst 11,997 patients in 14 hospitals exposed 2020-2023, 6.5% tested positive (median 3d after exposure). Positivity rates were 6.7% vs 5.8% for Omicron vs pre-Omicron exposures (<i>P</i> = 0.07) and 7.6% vs 4.6% for exposures before vs after admission (<i>P</i> < 0.001).</p>","PeriodicalId":13663,"journal":{"name":"Infection Control and Hospital Epidemiology","volume":" ","pages":"1-4"},"PeriodicalIF":3.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142371757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fidaxomicin versus oral vancomycin for <i>Clostridioides difficile</i> infection among patients at high risk for recurrence based on real-world experience.","authors":"Natasha N Pettit, Alison K Lew, Cynthia T Nguyen, Elizabeth Bell, Christopher J Lehmann, Jennifer Pisano","doi":"10.1017/ice.2024.145","DOIUrl":"https://doi.org/10.1017/ice.2024.145","url":null,"abstract":"<p><strong>Introduction: </strong><i>Clostridioides difficile</i> infection (CDI) is a common nosocomial infection and is associated with a high healthcare burden due to high rates of recurrence. In 2021 the IDSA/SHEA guideline update recommended fidaxomicin (FDX) as first-line therapy. Our medical center updated our institutional guidelines to follow these recommendations, prioritizing FDX use among patients at high risk for recurrent CDI (rCDI).</p><p><strong>Methods: </strong>This pre- post- quasi-experimental study included patients with a presumptive diagnosis of CDI at risk for recurrence (age >/= 65 years, immunocompromised, severe CDI) that received vancomycin (VAN) or FDX between October 2019 to October 2022. Patients who received bezlotoxumab, had fulminant CDI, or received <10 days of the same antibiotic for their full treatment course were excluded. Patients were evaluated for rCDI within 8 weeks of completion of therapy, subsequent episodes of CDI within 12 months, and CDI-related admissions within 30 days.</p><p><strong>Results: </strong>Of 397 CDI regimens evaluated, 196 received VAN and 201 received FDX. Rates of rCDI (9.2% vs 10%, <i>P</i> = 0.86), subsequent CDI within 12 months of therapy completion of therapy (19.4% vs 26%, <i>P</i> = 0.12) and 30-day CDI-related readmissions (3% vs 4.5%, <i>P</i> = 0.6) were similar between patients who received VAN versus FDX.</p><p><strong>Conclusion: </strong>Outcomes were similar between patients treated with FDX and VAN for the treatment of CDI among those at high risk for rCDI, using our outlined criteria. Although we observed a trend toward lower rates of rCDI among immunocompromised patients, this finding was not significant. Further investigation is needed to determine which patients with CDI may benefit from FDX.</p>","PeriodicalId":13663,"journal":{"name":"Infection Control and Hospital Epidemiology","volume":" ","pages":"1-7"},"PeriodicalIF":3.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142371755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging multi-database linkages to assess racial and ethnic disparities among Carbapenem-resistant Enterobacterales cases in Tennessee, 2015-2019.","authors":"Erika M Kirtz, Allison Chan, Kristina McClanahan, Rany Octaria","doi":"10.1017/ice.2024.86","DOIUrl":"https://doi.org/10.1017/ice.2024.86","url":null,"abstract":"<p><strong>Background: </strong>Awareness of health disparities' impact on clinical outcomes is increasing. However, public health's ability to highlight these trends can be limited by data missingness, such as on race and ethnicity. To better understand race and ethnicity's impact, we compared all-cause 30-day mortality rates between non-Hispanic (NH) Black, NH White, and Hispanic/NH other racial and ethnic patients among cases of carbapenem-resistant Enterobacterales (CRE).</p><p><strong>Methods: </strong>We performed data linkage using CRE statewide surveillance, Hospital Discharge Data System, and vital records data to obtain demographics and clinical outcomes on CRE cases in TN. We evaluated the association between race and ethnicity with all-cause 30-day mortality among CRE cases.</p><p><strong>Results: </strong>Among 2,804 reported CRE cases from 2015 to 2019, 65% (n = 1,832) were missing race and ethnicity; data linkage methods reduced missingness to 10% (n = 285). 22%, 74%, and 3% of cases were among NH Black, NH White, and Hispanic/NH other patients, respectively. Thirty-day all-cause mortality among NH Black patients was 5.7 per 100,000 population, 1.9 and 5.7 times higher than NH White and Hispanic/NH other patients. We observed that the risk of dying within 30 days of CRE diagnosis was 35% higher for NH Black compared to NH White patients; unmeasured confounders may be present (adjusted risk ratio 1.35; 95% CI 1.00, 1.83).</p><p><strong>Conclusion: </strong>Data linkage effectively reduced missingness of race and ethnicity. Among those with CRE, NH Blacks may have an increased risk of all-cause 30-day mortality. Data missingness creates barriers in identifying health disparities; data linkage is one approach to overcome this challenge.</p>","PeriodicalId":13663,"journal":{"name":"Infection Control and Hospital Epidemiology","volume":" ","pages":"1-7"},"PeriodicalIF":3.0,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142361467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}