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To evaluate whether pregnancy is an independent risk factor for in-hospital mortality among patients of reproductive age hospitalized with coronavirus disease 2019 (COVID-19) viral pneumonia.We conducted a retrospective cohort study (April 2020-May 2021) of 23,574 female inpatients aged 15-45 years with an International Classification of Diseases, Tenth Revision, Clinical Modification diagnosis code for COVID-19 discharged from 749 U.S. hospitals in the Premier Healthcare Database. We used a viral pneumonia diagnosis to select for patients with symptomatic COVID-19. The associations between pregnancy and in-hospital mortality, intensive care unit (ICU) admission, and mechanical ventilation were analyzed using propensity score-matched conditional logistic regression. Models were matched for age, marital status, race and ethnicity, Elixhauser comorbidity score, payer, hospital number of beds, season of discharge, hospital region, obesity, hypertension, diabetes mellitus, chronic pulmonary disease, deficiency anemias, depression, hypothyroidism, and liver disease.In-hospital mortality occurred in 1.1% of pregnant patients and 3.5% of nonpregnant patients hospitalized with COVID-19 and viral pneumonia (propensity score-matched odds ratio [OR] 0.39, 95% CI 0.25-0.63). The frequency of ICU admission for pregnant and nonpregnant patients was 22.0% and 17.7%, respectively (OR 1.34, 95% CI 1.15-1.55). Mechanical ventilation was used in 8.7% of both pregnant and nonpregnant patients (OR 1.05, 95% CI 0.86-1.29). Among patients who were admitted to an ICU, mortality was lower for pregnant compared with nonpregnant patients (OR 0.33, 95% CI 0.20-0.57), though mechanical ventilation rates were similar (35.7% vs 38.3%, OR 0.90, 95% CI 0.70-1.16). Among patients with mechanical ventilation, pregnant patients had a reduced risk of in-hospital mortality compared with nonpregnant patients (0.26, 95% CI 0.15-0.46).Despite a higher frequency of ICU admission, in-hospital mortality was lower among pregnant patients compared with nonpregnant patients with COVID-19 viral pneumonia, and these findings persisted after propensity score matching.

Background Hospital-onset bacteremia and fungemia (HOB) is being proposed as a potential healthcare quality measure due to its clinical significance, objectivity, and ease of detection. However, information is lacking on sources of HOB and the proportion and types of cases considered preventable. Methods We evaluated sources and potential preventability of HOB cases at 12 hospitals using retrospective chart review. HOB was defined as a positive blood culture >= day 4 (admission date=day 1) for one or more organisms. Stratified sampling of cases by commensal and noncommensal organisms was used. Infectious disease physicians determined the source of HOB and rated preventability from 1-6 (1=definitely preventable to 6=definitely not preventable) using a previously validated guide. Ratings of 1–3 were collectively considered potentially preventable and 4–6 likely not preventable. Results We evaluated sources and potential preventability of HOB cases at 12 hospitals using retrospective chart review. HOB was defined as a positive blood culture >= day 4 (admission date=day 1) for one or more organisms. Stratified sampling of cases by commensal and noncommensal organisms was used. Infectious disease physicians determined the source of HOB and rated preventability from 1–6 (1=definitely preventable to 6=definitely not preventable) using a previously validated guide. Ratings of 1–3 were collectively considered potentially preventable and 4–6 likely not preventable. Conclusion Gastrointestinal and endovascular sources account for a large majority of noncommensal HOB cases. A high proportion of noncommensal HOB cases are likely not preventable. The presence of non-preventable events should be considered when using HOB as a quality measure. Approaches to identifying the subset of preventable noncommensal HOB events should be explored. Disclosures Scott Fridkin, MD, Pfizer: Grant/Research Support Andi L. Shane, MD, MPH, MSc, International Scientific Association for Probiotics and Prebiotics (ISAPP): travel and lodging to attend international meeting to deliver a presentation, June 2022 Aaron Milstone, MD, Merck: Grant/Research Support Rajeshwari Nair, MBBS, PhD, Vertex Pharmaceuticals: Salary.

Background Understanding comorbidities that drive all-cause readmission in patients hospitalized with Coronavirus disease 2019 (COVID-19) can inform healthcare system capacity planning and improve post-discharge care. Methods This was a retrospective cohort study of patients hospitalized for COVID-19 between April 2020-December 2020 (index cohort) across 760 hospitals in the Premier Healthcare Database. Patients who died or left against medical advice were excluded from the index cohort. Surviving patients in the index cohort were followed until May 2021. First readmission to the same hospital as the COVID-19 index admission was considered all-cause readmission. The all-cause 14-month risk (95% confidence interval) of readmission was calculated using the Kaplan-Meier approach. A multivariable Cox proportional hazards model adjusted for demographic variables, hospital characteristics, co-existing comorbidities, and COVID-19 severity was built to study the association between Elixhauser comorbidities and readmission. Results Among 232155 unique patients in the index cohort, 36680 were readmitted to the same hospital at least once, followed through May 2021. The 14-month risk of readmission was 16.2% (95% CI:16.1% - 16.4%). The most frequent primary diagnosis on readmission was infectious disease (14240, 38.8%), of which 8754 (24%) were for COVID-19. With each additional comorbidity, the readmission hazard increased by 19% (HR, 1.19; 95% CI:1.18 - 1.19). In the multivariable Cox proportional hazards model, many comorbidity categories were associated with an increased risk of readmission. Metastatic cancer (HR, 1.74; 95% CI:1.60 –1.89), lymphoma (HR, 1.61; 95% CI:1.47 – 1.77), drug abuse (HR, 1.51; 95% CI:1.41 – 1.62), congestive heart failure (HR, 1.47; 95% CI:1.44– 1.51), and alcohol abuse (HR, 1.46; 95% CI:1.36– 1.56) were associated with the highest hazard for readmission. Conclusion COVID-19 patients have a high risk of all-cause readmission and are frequently readmitted for COVID-19. With the continued emergence of COVID-19 variants, this study provides valuable insights into developing more informed discharge plans and improving post-discharge care for COVID-19 patients with existing comorbidities to prevent readmission. Disclosures Lyndsay M. O'Hara, PhD, MPH, PDI: Grant/Research Support.

Objective: Hospital readmission is unsettling to patients and caregivers, costly to the healthcare system, and may leave patients at additional risk for hospital-acquired infections and other complications. We evaluated the association between comorbidities present during index coronavirus disease 2019 (COVID-19) hospitalization and the risk of 30-day readmission. Design, setting, and participants: We used the Premier Healthcare database to perform a retrospective cohort study of COVID-19 hospitalized patients discharged between April 2020 and March 2021 who were followed for 30 days after discharge to capture readmission to the same hospital. Results: Among the 331,136 unique patients in the index cohort, 36,827 (11.1%) had at least 1 all-cause readmission within 30 days. Of the readmitted patients, 11,382 (3.4%) were readmitted with COVID-19 as the primary diagnosis. In the multivariable model adjusted for demographics, hospital characteristics, coexisting comorbidities, and COVID-19 severity, each additional comorbidity category was associated with an 18% increase in the odds of all-cause readmission (adjusted odds ratio [aOR], 1.18; 95% confidence interval [CI], 1.17–1.19) and a 10% increase in the odds of readmission with COVID-19 as the primary readmission diagnosis (aOR, 1.10; 95% CI, 1.09–1.11). Lymphoma (aOR, 1.86; 95% CI, 1.58–2.19), renal failure (aOR, 1.32; 95% CI, 1.25–1.40), and chronic lung disease (aOR, 1.29; 95% CI, 1.24–1.34) were most associated with readmission for COVID-19. Conclusions: Readmission within 30 days was common among COVID-19 survivors. A better understanding of comorbidities associated with readmission will aid hospital care teams in improving postdischarge care. Additionally, it will assist hospital epidemiologists and quality administrators in planning resources, allocating staff, and managing bed-flow issues to improve patient care and safety.

Background Published bundles to reduce Clostridioides difficile Infection (CDI) frequently lack information on compliance with individual elements. We piloted a computerized clinical decision support-based intervention bundle and conducted detailed evaluation of several intervention-related measures. Methods A quasi-experimental study of a bundled intervention was performed at 2 acute care community hospitals in Maryland. The bundle had five components: (1) timely placement in enteric precautions, (2) appropriate CDI testing, (3) reducing proton-pump inhibitor (PPI) use, (4) reducing high-CDI risk antibiotic use, and (5) optimizing use of a sporicidal agent for environmental cleaning. Chi-square and Kruskal-Wallis tests were used to compare measure differences. An interrupted time series analysis was used to evaluate impact on hospital-onset (HO)-CDI. Results Placement of CDI suspects in enteric precautions before test results did not change. Only hospital B decreased the frequency of CDI testing and reduced inappropriate testing related to laxative use. Both hospitals reduced the use of PPI and high-risk antibiotics. A 75% decrease in HO-CDI immediately postimplementation was observed for hospital B only. Conclusion A CDI reduction bundle showed variable impact on relevant measures. Hospital-specific differential uptake of bundle elements may explain differences in effectiveness, and emphasizes the importance of measuring processes and intermediate outcomes.

Hospitalized patients with SARS-CoV-2 infection (COVID-19) often receive antibiotics for suspected bacterial coinfection. We estimated the incidence of bacterial coinfection and secondary infection in COVID-19 using clinical diagnoses to determine how frequently antibiotics are administered when bacterial infection is absent. We performed a retrospective cohort study of inpatients with COVID-19 present on admission to hospitals in the Premier Healthcare Database between April and June 2020. Bacterial infections were defined using ICD-10-CM diagnosis codes and associated “present on admission” coding. Coinfections were defined by bacterial infection present on admission, while secondary infections were defined by bacterial infection that developed after admission. Coinfection and secondary infection were not mutually exclusive. A total of 18.5% of 64,961 COVID-19 patients (n = 12,040) presented with bacterial infection at admission, 3.8% (n = 2,506) developed secondary infection after admission, and 0.9% (n = 574) had both; 76.3% (n = 49,551) received an antibiotic while hospitalized, including 71% of patients who had no diagnosis of bacterial infection. Secondary bacterial infection occurred in 5.7% of patients receiving steroids in the first 2 days of hospitalization, 9.9% receiving tocilizumab in the first 2 days of hospitalization, and 10.3% of patients receiving both. After adjusting for patient and hospital characteristics, bacterial coinfection (adjusted relative risk [aRR], 1.15; 95% confidence interval [CI], 1.11 to 1.20) and secondary infection (aRR 1.93; 95% CI, 1.82 to 2.04) were both independently associated with increased mortality. Although 1 in 5 inpatients with COVID-19 presents with bacterial infection, secondary infections in the hospital are uncommon. Most inpatients with COVID-19 receive antibiotic therapy, including 71% of those not diagnosed with bacterial infection.

The transmission rate of methicillin-resistant Staphylococcus aureus (MRSA) to gloves or gowns of healthcare personnel (HCP) caring for MRSA patients in a non–intensive care unit setting was 5.4%. Contamination rates were higher among HCP performing direct patient care and when patients had detectable MRSA on their body. These findings may inform risk-based contact precautions.

OBJECTIVE: To determine whether electronically available comorbidities and laboratory values on admission are risk factors for hospital-onset Clostridioides difficile infection (HO-CDI) across multiple institutions and whether they could be used to improve risk adjustment. PATIENTS: All patients at least 18 years of age admitted to 3 hospitals in Maryland between January 1, 2016, and January 1, 2018. METHODS: Comorbid conditions were assigned using the Elixhauser comorbidity index. Multivariable log-binomial regression was conducted for each hospital using significant covariates (P < .10) in a bivariate analysis. Standardized infection ratios (SIRs) were computed using current Centers for Disease Control and Prevention (CDC) risk adjustment methodology and with the addition of Elixhauser score and individual comorbidities. RESULTS: At hospital 1, 314 of 48,057 patient admissions (0.65%) had a HO-CDI; 41 of 8,791 patient admissions (0.47%) at community hospital 2 had a HO-CDI; and 75 of 29,211 patient admissions (0.26%) at community hospital 3 had a HO-CDI. In multivariable regression, Elixhauser score was a significant risk factor for HO-CDI at all hospitals when controlling for age, antibiotic use, and antacid use. Abnormal leukocyte level at hospital admission was a significant risk factor at hospital 1 and hospital 2. When Elixhauser score was included in the risk adjustment model, it was statistically significant (P < .01). Compared with the current CDC SIR methodology, the SIR of hospital 1 decreased by 2%, whereas the SIRs of hospitals 2 and 3 increased by 2% and 6%, respectively, but the rankings did not change. CONCLUSIONS: Electronically available patient comorbidities are important risk factors for HO-CDI and may improve risk-adjustment methodology.

We studied the association between chlorhexidine gluconate (CHG) concentration on skin and resistant bacterial bioburden. CHG was almost always detected on the skin, and detection of methicillin-resistant Staphylococcus aureus, carbapenem-resistant Enterobacteriaceae, and vancomycin-resistant Enterococcus on skin sites was infrequent. However, we found no correlation between CHG concentration and bacterial bioburden.

Background: Hospital-onset bacteremia and fungemia (HOB) may be a preventable hospital-acquired condition and a potential healthcare quality measure. We developed and evaluated a tool to assess the preventability of HOB and compared it to a more traditional consensus panel approach. Methods: A 10-member healthcare epidemiology expert panel independently rated the preventability of 82 hypothetical HOB case scenarios using a 6-point Likert scale (range, 1= “Definitively or Almost Certainly Preventable” to 6= “Definitely or Almost Certainly Not Preventable”). Ratings on the 6-point scale were collapsed into 3 categories: Preventable (1–2), Uncertain (3–4), or Not preventable (5–6). Consensus was defined as concurrence on the same category among ≥70% expert raters. Cases without consensus were deliberated via teleconference, web-based discussion, and a second round of rating. The proportion meeting consensus, overall and by predefined HOB source attribution, was calculated. A structured HOB preventability rating tool was developed to explicitly account for patient intrinsic and extrinsic healthcare-related risks (Fig. 1). Two additional physician reviewers independently applied this tool to adjudicate the same 82 case scenarios. The tool was iteratively revised based on reviewer feedback followed by repeat independent tool-based adjudication. Interrater reliability was evaluated using the Kappa statistic. Proportion of cases where tool-based preventability category matched expert consensus was calculated. Results: After expert panel round 1, consensus criteria were met for 29 cases (35%), which increased to 52 (63%) after round 2. Expert consensus was achieved more frequently for respiratory or surgical site infections than urinary tract and central-line–associated bloodstream infections (Fig. 2a). Most likely to be rated preventable were vascular catheter infections (64%) and contaminants (100%). For tool-based adjudication, following 2 rounds of rating with interim tool revisions, agreement between the 2 reviewers was 84% for cases overall (κ, 0.76; 95% CI, 0.64–0.88]), and 87% for the 52 cases with expert consensus (κ, 0.79; 95% CI, 0.65–0.94). Among cases with expert consensus, tool-based rating matched expert consensus in 40 of 52 (77%) and 39 of 52 (75%) cases for reviewer 1 and reviewer 2, respectively. The proportion of cases rated “uncertain“ was lower among tool-based adjudicated cases with reviewer agreement (15 of 69) than among cases with expert consensus (23 of 52) (Fig. 2b). Conclusions: Healthcare epidemiology experts hold varying perspectives on HOB preventability. Structured tool-based preventability rating had high interreviewer reliability, matched expert consensus in most cases, and rated fewer cases with uncertain preventability compared to expert consensus. This tool is a step toward standardized assessment of preventability in future HOB evaluations.Funding: NoneDisclosures: None

Background: Estimates of contamination of healthcare personnel (HCP) gloves and gowns with methicillin-resistant Staphylococcus aureus (MRSA) following interactions with colonized or infected patients range from 17% to 20%. Most studies were conducted in the intensive care unit (ICU) setting where patients had a recent positive clinical culture. The aim of this study was to determine the rate of MRSA transmission to HCP gloves and gown in non-ICU acute-care hospital units and to identify associated risk factors. Methods: Patients on contact precautions with history of MRSA colonization or infection admitted to non-ICU settings were randomly selected from electronic health records. We observed patient care activities and cultured the gloves and gowns of 10 HCP interactions per patient prior to doffing. Cultures from patients’ anterior nares, chest, antecubital fossa and perianal area were collected to quantify bacterial bioburden. Bacterial counts were log transformed. Results: We observed 55 patients (Fig. 1), and 517 HCP–patient interactions. Of the HCP–patient interactions, 16 (3.1%) led to MRSA contamination of HCP gloves, 18 (3.5%) led to contamination of HCP gown, and 28 (5.4%) led to contamination of either gloves or gown. In addition, 5 (12.8%) patients had a positive clinical or surveillance culture for MRSA in the prior 7 days. Nurses, physicians and technicians were grouped in “direct patient care”, and rest of the HCPs were included in “no direct care group.” Of 404 interactions, 26 (6.4%) of providers in the “direct patient care” group showed transmission of MRSA to gloves or gown in comparison to 2 of 113 (1.8%) interactions involving providers in the “no direct patient care” group (P = .05) (Fig. 2). The median MRSA bioburden was 0 log 10CFU/mL in the nares (range, 0–3.6), perianal region (range, 0–3.5), the arm skin (range, 0-0.3), and the chest skin (range, 0–6.2). Detectable bioburden on patients was negatively correlated with the time since placed on contact precautions (rs= −0.06; P < .001). Of 97 observations with detectable bacterial bioburden at any site, 9 (9.3%) resulted in transmission of MRSA to HCP in comparison to 11 (3.6%) of 310 observations with no detectable bioburden at all sites (P = .03). Conclusions: Transmission of MRSA to gloves or gowns of HCP caring for patients on contact precautions for MRSA in non-ICU settings was lower than in the ICU setting. More evidence is needed to help guide the optimal use of contact precautions for the right patient, in the right setting, for the right type of encounter.Funding: NoneDisclosures: None

Background: Methicillin-resistant Staphylococcus aureus (MRSA) is associated with difficult to treat infections and high levels of morbidity. It is importance to assess the effectiveness of surveillance screening programs in prevention of MRSA infection. The objective of the study was to assess the type, pattern and antimicrobial sensitivity of MRSA infection and analyse the effectiveness of preventive measures in reduction of MRSA infection rate from 2014 to 2015. Methods: 1044 and 996 samples with positive cultures obtained from patients admitted in 2014 and 2015 respectively were screened for MRSA using chrome agar test. Only MRSA positive cultures were included in the study and their sensitivity to antibiotics was tested. Screening of MRSA infection was conducted in patients as well as staff of Rao Nursing home, Pune for early identification of MRSA infection and prevention of transmission. MRSA infection rates in 2014 and 2015 were compared. Results: Community acquired MRSA (CA-MRSA) was more common when compared to Hospital acquired pneumonia (HA-MRSA). HA-MRSA was more common in patients admitted in isolation units. There was a decrease in number of MRSA positive cultures from 2014 (4.8%) to 2015 (1.3%), proving the effectiveness of screening for MRSA infection amongst patients as well as healthcare workers. Higher hand hygiene rates were observed in 2015 (95.83%), which further contributed to the decrease in incidence of MRSA infection in 2015. Conclusions: Strict adherence to preventive measures of MRSA such as hand hygiene, monitoring and adherence to the bundles for prevention with judicious use of antibiotics can greatly reduce the incidence of MRSA infection.

Background Hospital-onset C. difficile infection (HO-CDI) rates are publicly reported. However, patient-level risk factors are not included in the current risk adjustment methodology, and the knowledge as to which risk factors to include is incomplete. This study aimed to determine whether electronically-available comorbidities and laboratory indicators of severity of illness are risk factors for HO-CDI. Methods We performed a retrospective cohort study of all adult patients admitted to three hospitals (one academic, two community) in Baltimore, Maryland between January 1, 2016 and January 1, 2018. Information extracted from electronic medical records included demographics, ICD-10 codes, laboratory results within 24 hours of admission (i.e., hematocrit, hemoglobin, platelet count, leukocytes, BUN, CO2, creatinine, glucose, sodium, and potassium), medication administration (i.e., antibiotic and antacid use), and C. difficile test result. Comorbid conditions were assessed by the Elixhauser Comorbidity Index components. HO-CDI was defined by positive laboratory test > 3 days after admission. Potential risk factors for HO-CDI were assessed using bivariate log binomial regression. Multivariable log binomial regression was conducted using significant (P < 0.1) covariates. Results At hospital 1 (academic), 314 of the 48,057 (0.65%) eligible patient admissions had HO- CDI; 41 of the 8,791 (0.47%) and 75 of the 29,211 (0.26%) of patient admissions at community hospitals 2 and 3, respectively, had HO-CDI. In multivariable analysis, Elixhauser Score was a significant risk factor for HO-CDI at all hospitals when controlling for antibiotic and antacid use; for every one-point increase in Elixhauser Score, there was an increased risk of HO-CDI of 1.27 (95% CI: 1.21, 1.32) at hospital 1, 1.38 (95% CI: 1.24, 1.54) at hospital 2, and 1.28 (95% CI: 1.10, 1.31) at hospital 3. Table 1 shows significant risk factors for HO-CDI for each hospital. When individual comorbidities were assessed in the regression analysis, fluid and electrolyte disorders were a significant risk factor for HO-CDI for all hospitals. Conclusion Laboratory values upon admission and electronically available patient comorbidities are important risk factors for HO-CDI and should be considered for future risk adjustment. Disclosures All authors: No reported disclosures.

Maghsoudi, Behzad/0000-0002-1279-8799; Kritsotakis, Evangelos/0000-0002-9526-3852; Yalcin, Ata Nevzat/0000-0002-7243-7354; Mikaszewska-Sokolewicz, Malgorzata A/0000-0002-1148-7817; ALDEMIR KOCABAS, Bilge/0000-0002-6396-5243; Masjedi, Mohammad Reza/0000-0002-6871-382X; Masjedi, Mansoor/0000-0001-6175-9289; Gan, Chin Seng/0000-0002-6758-4798; Oncul, Oral/0000-0002-1681-1866; Sabetian, Golnar/0000-0001-8764-2150; YAMAN, Ayhan/0000-0002-5651-1286; Ozdemir, Halil/0000-0002-7318-1688; Leon, Hector Armando Rincon/0000-0003-0715-200X; Delia, Muntean/0000-0001-9100-4530; Atasay, Fatma Begum/0000-0002-9114-5293; Zand, Farid/0000-0003-3489-3372; Kuchenbecker, Ricardo/0000-0002-4707-3683; Medeiros, Eduardo A/0000-0002-6205-259X; ROMERO LOPEZ-ALBERCA, CRISTINA/0000-0001-5856-8668; Salomao, Reinaldo/0000-0003-1149-4598; ozdemir, sonay incesoy/0000-0003-2863-901X; Meco, Basak Ceyda/0000-0003-2951-9634; Horoz, Ozden Ozgur/0000-0001-7590-650X; Horhat, Florin George/0000-0001-6133-0204; Kazi, Mohammad Mukhit/0000-0003-3824-5540; Ciftci, Ergin/0000-0002-4955-160X; Abouqal, Redouane/0000-0002-6117-4341; Leblebicioglu, Hakan/0000-0002-6033-8543; Garcell, Humberto Guanche/0000-0001-7279-0062; Kendirli, Tanil/0000-0001-9458-2803; de Souza, Maria Cecilia B V/0000-0003-0318-3087; Sanaei Dashti, Anahita/0000-0002-2827-3575; UNAL, SERHAT/0000-0003-1184-4711; alvarez Moreno, carlos Arturo/0000-0001-5419-4494; Barahona G., Nayide/0000-0003-3559-6900; Rodriguez Ferrer, Marena Luz/0000-0002-8053-8454; Karabay, Oguz/0000-0003-0502-432X; Kaya, Sehnaz/0000-0003-0002-1517; ERDEVE, OMER/0000-0002-3193-0812; Gonzalez Martinez, Marisela del Rocio/0000-0003-1474-736X; Gikas, Achilleas/0000-0002-8455-9631; Baylan, Orhan/0000-0002-6529-7824; Mitrev, Zan/0000-0001-7859-8821; yildizdas, dincer/0000-0003-0739-5108; Ngo, Quy Chau/0000-0002-6787-2757; Duszynska, Wieslawa/0000-0002-5880-4904; KAYA, ZEYNEP/0000-0002-8468-2103; Dragan, Barbara/0000-0003-3108-4211; Gupta, Umesh/0000-0001-7717-1404; Navarro Fuentes, Karla Reyna/0000-0002-6894-8872; zali, Mohammadreza/0000-0002-9027-4560; Morocho Tutillo, Diego Rolando/0000-0002-4974-7856; El Kholy, Amani/0000-0002-0645-7664; Kanj, Souha/0000-0001-6413-3396; Assiri, Abdullah/0000-0002-5605-2876; Unal, Necmettin/0000-0002-9440-7893; Jayatilleke, Kushlani/0000-0002-3931-6630; KARABAY, OGUZ/0000-0003-1514-1685 WOS: 000341307100001 PubMed: 25179325 We report the results of an International Nosocomial Infection Control Consortium (INICC) surveillance study from January 2007-December 2012 in 503 intensive care units (ICUs) in Latin America, Asia, Africa, and Europe. During the 6-year study using the Centers for Disease Control and Prevention's (CDC) U.S. National Healthcare Safety Network (NHSN) definitions for device-associated health care-associated infection (DA-HAI), we collected prospective data from 605,310 patients hospitalized in the INICC's ICUs for an aggregate of 3,338,396 days. Although device utilization in the INICC's ICUs was similar to that reported from ICUs in the U.S. in the CDC's NHSN, rates of device-associated nosocomial infection were higher in the ICUs of the INICC hospitals: the pooled rate of central line-associated bloodstream infection in the INICC's ICUs, 4.9 per 1,000 central line days, is nearly 5-fold higher than the 0.9 per 1,000 central line days reported from comparable U. S. ICUs. The overall rate of ventilator-associated pneumonia was also higher (16.8 vs 1.1 per 1,000 ventilator days) as was the rate of catheter-associated urinary tract infection (5.5 vs 1.3 per 1,000 catheter days). Frequencies of resistance of Pseudomonas isolates to amikacin (42.8% vs 10%) and imipenem (42.4% vs 26.1%) and Klebsiella pneumoniae isolates to ceftazidime (71.2% vs 28.8%) and imipenem (19.6% vs 12.8%) were also higher in the INICC's ICUs compared with the ICUs of the CDC's NHSN. Copyright (C) 2014 by the Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.