bluuebunny/crossref_metadata_2025_split huggingface dataset:
Large Multisite Clinical Field Study Characterizing Contamination Levels in Patient Used Endoscopes After Manual Cleaning
Authors: Bommarito Marco , Meyer Mark 
Published: November 2020

Background: Multiple outbreaks multidrug-resistant organisms (MDROs) have been associated with flexible endoscopes resulting in unacceptable patient mortality and morbidity. Evidence highlights the importance of effective cleaning to achieve effective high-level disinfection (HLD). This study presents an analysis of >700,000 measurements of adenosine-triphosphate (ATP) contamination levels found in flexible endoscopes after manual cleaning. Method: This 2018–2019 study consists of 702,768 measurements of ATP levels found in the suction/biopsy channel of instruments used on patients after manual cleaning: gastroscopes (267,533 measurements from 223 sites), duodenoscopes (123,697 measurements from 161 sites), colonoscopes (252,249 measurements from 229 sites), and bronchoscopes (59,289 measurements from 107 sites). Sites were located across the United States and employed protocols that included routine cleaning verification performed by the reprocessing technicians using a handheld luminometer and the associated ATP water test (3M Clean-Trace). Results: Figure 1 shows a boxplot analysis of the ATP levels by endoscope type. Upper gastrointestinal (GI) endoscopes (gastroscopes and duodenoscopes) show a significantly (P < .005) greater level of ATP contamination after manual cleaning. The pairwise mean differences are all significant (P < .005) except for colonoscopes when compared to bronchoscopes (P = .203). Also shown on Fig. 1 is a literature supported adequate cleanliness value of 200 RLUs [=2.3log(RLUs)] (MJ Alfa et al.; Am J Infec Control 2013;41:245–253 and ANSI/AAMI ST91; 2015). A 95% confidence interval analysis performed against this literature value (Table 1) showed that a high number of gastroscopes (12%) and duodenoscopes (10%) are not adequately clean. Figure 2 shows a box-plot analysis of the data set by endoscope type and by site. There is significant (P < .005) site-to-site variability for all endoscope types as demonstrated by variation in mean values, box size, and many outliers. Conclusions: This study highlights the importance of using a quantitative cleaning verification method to better understand process capability and to provide more robust quality assurance for manual cleaning. Significant differences were detected in the level of cleanliness between upper GI scopes and lower GI scopes and bronchoscopes. When compared to a literature-supported level for adequate cleanliness, upper GI scopes exhibited failure rates in excess of 10%. Furthermore, significant site-to-site variability occurred, and many outliers fell well beyond the normal process envelope, representing significant cleaning lapses. Root causes to these concerning findings could range from inadequate execution of the cleaning protocol, to device design, to age and existing damage that could prevent achieving adequate cleaning and possibly impair the effectiveness of HLD.Funding: NoneDisclosures: Marco Bommarito, 3M (salary)
Identifying Opportunities to Improve Accuracy of NHSN Reporting: Lessons Learned From State Health Department Validations.
Authors: Bagchi Suparna , Norrick Bonnie , Lewis Nigel 
Published: November 2020

Background: State Health Departments (SHDs) have systematically studied the validity of healthcare-associated infection (HAI) surveillance data submitted by healthcare facilities in their jurisdictions to the Centers for Disease Control and Prevention’s (CDC’s) National Healthcare Safety Network (NHSN) for central-line–associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), surgical site infections following colon and abdominal hysterectomy procedures (SSI COLO and HYST), methicillin-resistant Staphylococcus aureus and Clostridioides difficile laboratory identified (MRSA and CDI LabID respectively) events. These studies are a key source of information about data quality and completeness serving as an impetus and a guide for improving the caliber of NHSN’s HAI data. Methods: We contacted SHD HAI coordinators in all states for an inventory of HAI validation studies. We used data from these studies to calculate pooled mean sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for HAI case determinations. HAI case reporting “error rates” were computed as the proportion of mismatches (underreport and overreport) among the medical records reviewed by SHDs and reasons for misclassification were categorized. Results: SHD validation studies varied by HAI type (range, 4 studies for MRSA LabID and 23 for CLABSI). Pooled mean sensitivity of HAI reporting ranged from 73.1% (COLO SSI) to 92.7% (CDI LabID). Pooled mean specificity and PPV exceeded 90% for all HAIs. LabID event validations demonstrated the lowest NPV (58.8% for MRSA and 55.1% for CDI). Error rates of HAI reporting to NHSN ranged from 2.5% (HYST SSI) to 13.6% (MRSA LabID). Common errors identified during CLABSI and CAUTI validations were incorrect application of general NHSN and CLABSI- and CAUTI-specific definitions. Incorrect secondary BSI attribution was the most frequently identified reason by CLABSI SHD validations (64.7%). Of all operative procedure-associated misclassifications, inconsistent surveillance practices (66.6%), incorrect NHSN operative procedure category assignment (55.5%), and misapplication of general organ-space and/or site-specific infection criteria (44.4%) were identified as the most common shortcomings. Among MRSA and CDI LabID validations, missed case finding due to failure to review candidate events and gaps in understanding the 14-day reporting rule of LabID protocol were identified as predominant reasons for inaccurate reporting. Conclusions: SHD HAI data validations identified specific targets for additional surveillance training, especially CLABSI determinations and application of the protocol rules for MDRO/CDI LabID case determinations. Further work is also needed to assure that data sources in addition to wound cultures are used for SSI determinations and that postdischarge SSI surveillance is more vigorous and comprehensive.Funding: NoneDisclosures: None
Validation of a Surgical Site Infection Detection Algorithm for Use in Cardiac and Orthopedic Surgery Research
Authors: Suzuki Hiroyuki , Balkenende Erin , Perencevich Eli , Clore Gosia , Richardson Kelly , Nair Rajeshwari , Goto Michihiko , Branch-Elliman Westyn , Gupta Kalpana , Sherlock Stacey Hockett , Schweizer Marin 
Published: November 2020

Background: Studies of interventions to decrease rates of surgical site infections (SSIs) must include thousands of patients to be statistically powered to demonstrate a significant reduction. Therefore, it is important to develop methodology to extract data available in the electronic medical record (EMR) to accurately measure SSI rates. Prior studies have created tools that optimize sensitivity to prioritize chart review for infection control purposes. However, for research studies, positive predictive value (PPV) with reasonable sensitivity is preferred to limit the impact of false-positive results on the assessment of intervention effectiveness. Using information from the prior tools, we aimed to determine whether an algorithm using data available in the Veterans Affairs (VA) EMR could accurately and efficiently identify deep incisional or organ-space SSIs found in the VA Surgical Quality Improvement Program (VASQIP) data set for cardiac and orthopedic surgery patients. Methods: We conducted a retrospective cohort study of patients who underwent cardiac surgery or total joint arthroplasty (TJA) at 11 VA hospitals between January 1, 2007, and April 30, 2017. We used EMR data that were recorded in the 30 days after surgery on inflammatory markers; microbiology; antibiotics prescribed after surgery; International Classification of Diseases (ICD) and current procedural terminology (CPT) codes for reoperation for an infection related purpose; and ICD codes for mediastinitis, prosthetic joint infection, and other SSIs. These metrics were used in an algorithm to determine whether a patient had a deep or organ-space SSI. Sensitivity, specificity, PPV and negative predictive values (NPV) were calculated for accuracy of the algorithm through comparison with 30-day SSI outcomes collected by nurse chart review in the VASQIP data set. Results: Among the 11 VA hospitals, there were 18,224 cardiac surgeries and 16,592 TJA during the study period. Of these, 20,043 were evaluated by VASQIP nurses and were included in our final cohort. Of the 8,803 cardiac surgeries included, manual review identified 44 (0.50%) mediastinitis cases. Of the 11,240 TJAs, manual review identified 71 (0.63%) deep or organ-space SSIs. Our algorithm identified 32 of the mediastinitis cases (73%) and 58 of the deep or organ-space SSI cases (82%). Sensitivity, specificity, PPV, and NPV are shown in Table 1. Of the patients that our algorithm identified as having a deep or organ-space SSI, only 21% (PPV) actually had an SSI after cardiac surgery or TJA. Conclusions: Use of the algorithm can identify most complex SSIs (73%–82%), but other data are necessary to separate false-positive from true-positive cases and to improve the efficiency of case detection to support research questions.Funding: NoneDisclosures: None
A Machine-Learning Approach For Predicting Antibiotic Resistance in Pseudomonas aeruginosa
Authors: Magleby Reed , Simon Matthew , Calfee David , Zachariah Philip , Cohen Bevin , Cazer Casey , Wang Fei , Larson Elaine 
Published: November 2020

Background:Pseudomonas aeruginosa is an important nosocomial pathogen associated with intrinsic and acquired resistance mechanisms to major classes of antibiotics. To better understand clinical risk factors for drug-resistant P. aeruginosa infection, decision-tree models for the prediction of fluoroquinolone and carbapenem-resistant P. aeruginosa were constructed and compared to multivariable logistic regression models using performance characteristics. Methods: In total, 5,636 patients admitted to 4 hospitals within a New York City healthcare system from 2010 to 2016 with blood, respiratory, wound, or urine cultures growing PA were included in the analysis. Presence or absence of drug-resistance was defined using the first culture of any source positive for P. aeruginosa during each hospitalization. To train and validate the prediction models, cases were randomly split (60 of 40) into training and validation datasets. Clinical decision-tree models for both fluoroquinolone and carbapenem resistance were built from the training dataset using 21 clinical variables of interest, and multivariable logistic regression models were built using the 16 clinical variables associated with resistance in bivariate analyses. Decision-tree models were optimized using K-fold cross validation, and performance characteristics between the 4 models were compared. Results: From 2010 through 2016, prevalence of fluoroquinolone and carbapenem resistance was 32% and 18%, respectively. For fluoroquinolone resistance, the logistic regression algorithm attained a positive predictive value (PPV) of 0.57 and a negative predictive value (NPV) of 0.73 (sensitivity, 0.27; specificity, 0.90) and the decision-tree algorithm attained a PPV of 0.65 and an NPV of 0.72 (sensitivity 0.21, specificity 0.95). For carbapenem resistance, the logistic regression algorithm attained a PPV of 0.53 and a NPV of 0.85 (sensitivity 0.20, specificity 0.96) and the decision-tree algorithm attained a PPV of 0.59 and an NPV of 0.84 (sensitivity 0.22, specificity 0.96). The decision-tree partitioning algorithm identified prior fluoroquinolone resistance, SNF stay, sex, and length-of-stay as variables of greatest importance for fluoroquinolone resistance compared to prior carbapenem resistance, age, and length-of-stay for carbapenem resistance. The highest-performing decision tree for fluoroquinolone resistance is illustrated in Fig. 1. Conclusions: Supervised machine-learning techniques may facilitate prediction of P. aeruginosa resistance and risk factors driving resistance patterns in hospitalized patients. Such techniques may be applied to readily available clinical information from hospital electronic health records to aid with clinical decision making.Funding: NoneDisclosures: None
A Simple Cleaning Intervention to Prevent Transmission of Carbapenemase-Producing Enterobacterales from Hospital Sinks
Authors: Kwong Jason , Leroi Marcel , Bannam Trudi , Edmonds Deidre , Grabsch Elizabeth , Narayanasamy Shanti , Greenough John , Lane Courtney , Easton Marion , Howden Benjamin , Johnson Paul , Grayson M. 
Published: November 2020

Background: A prolonged outbreak of carbapenemase-producing Serratia marcescens (CPSM) was identified in our quaternary healthcare center over a 2-year period from 2015 through 2017. A reservoir of IMP-4–producing S. marcescens in sink drains of clinical hand basins (CHB) was implicated in propagating transmission, supported by evidence from whole-genome sequencing (WGS). We assessed the impact of manual bioburden reduction intervention on further transmission of CPSM. Methods: Environmental sampling of frequently touched wet and dry areas around CPSM clinical cases was undertaken to identify potential reservoirs and transmission pathways. After identifying CHB as a source of CPSM, a widespread annual CHB cleaning intervention involving manual scrubbing of sink drains and the proximal pipes was implemented. Pre- and postintervention point prevalence surveys (PPS) of CHB drains performed to assess for CPSM colonization. Surveillance for subsequent transmission was conducted through weekly screening of patients and annual screening of CHB in transmission areas, and 6-monthly whole-hospital PPS of patients. All CPSM isolates were assessed by WGS. Results: In total, 6 patients were newly identified with CPSM from 2015 to 2017 (4.3 transmission events per 100,000 surveillance bed days [SBD]; 95% CI, 1.6–9.4). All clinical CPSM isolates were linked to CHB isolates by WGS. The CHB cleaning intervention resulted in a reduction in CHB colonization with CPSM in transmission areas from 72% colonization to 28% (ARR, 0.44; 95% CI, 0.25–0.63). A single further clinical case of CPSM linked to the CHB isolates was detected over 2 years of surveillance from 2017 to 2019 following the implementation of the annual CHB cleaning program (0.7 transmissions per 100,000 SBD; 95% CI, 0.0–3.9). No transmissions were linked to undertaking the cleaning intervention. Conclusions: A simple intervention targeted at reducing the biological burden of CPSM in CHB drains at regular intervals was effective in preventing transmission of carbapenemase-producing Enterobacterales from the hospital environment to patients over a prolonged period of intensive surveillance. These findings highlight the importance of detailed cleaning for controlling the spread of multidrug-resistant organisms from healthcare environments.Funding: NoneDisclosures: Jason Kwong, Austin Health
Association Between Federal Value-Based Incentive Program Implementation and Hospital-Onset C. difficile Infection Rates
Authors: Alrawashdeh Mohammad , Rhee Chanu , Hsu Heather , Lee Grace 
Published: November 2020

Background: The Hospital-Acquired Conditions Reduction Program (HACRP) and Hospital Value-Based Purchasing (HVBP) are federal value-based incentive programs that financially reward or penalize hospitals based on quality metrics. Hospital-onset C. difficile infection (HO-CDI) rates reported to the CDC NHSN became a target quality metric for both HACRP and HVBP in October 2016, but the impact of these programs on HO-CDI rates is unknown. Methods: We used an interrupted time-series design to examine the association between HACRP/HVBP implementation in October 2016 and quarterly rates of HO-CDI per 10,000 patient days among incentive-eligible acute-care hospitals conducting facility-wide HO-CDI NHSN surveillance between January 2013 and March 2019. Generalized estimating equations were used to fit negative binomial regression models to assess for immediate program impact (ie, level change) and changes in the slope of HO-CDI rates, controlling for each hospital’s predominant method for CDI testing (nucleic acid amplification including PCR (NAAT), enzyme immunoassay for toxin (EIA), or other testing method including cell cytotoxicity neutralization assay and toxigenic culture). Results: Of the 265 study hospitals studied, most were medium-sized (100–399 beds, 55%), not-for-profit (77%), teaching hospitals (70%), and were located in a metropolitan area (87%). Compared to EIA, rates of HO-CDI were higher when detected by NAAT (incidence rate ratio [IRR], 1.55; 95% CI, 1.41–1.70) or other testing methods (IRR, 1.47; 95% CI, 1.26–1.71). Controlling for CDI testing methods, HACRP/HVBP implementation was associated with an immediate 6% decline in HO-CDI rates (IRR, 0.94; 95% CI, 0.89–0.99) and a 4% decline in slope per year-quarter thereafter (IRR, 0.96; 95% CI, 0.95–0.97) (Fig. 1). Conclusions: HACRP/HVBP implementation was associated with both immediate and gradual improvements in HO-CDI rates, independent of CDI testing methods of differing sensitivity. Future research may evaluate the precise mechanisms underlying this improvement and if this impact is sustained in the long term.Funding: NoneDisclosures: None
Comparison of Matched Patient Data for SSIs following Total Hip and Total Knee Arthoplasty: IPC Versus NSQIP Surveillance
Authors: Ellison Jennifer , Chakravorty David , Conly John , Kim Joseph , Litvinchuk Stacey , Pokhrel Arun , Shen Ye , Smith Christopher , Bush Kathryn 
Published: November 2020

Background: In Alberta, Canada, surgical site infections (SSIs) following total hip and knee replacements (THRs and TKRs) are reported using the infection prevention and control (IPC) surveillance system, which surveys all THRs and TKRs using the NHSN definitions; and the National Surgical Quality Improvement Program (NSQIP), which uses different definitions and sampling strategies. Deterministic matching of patient data from these sources was used to examine the overlap and discrepancies in SSI reporting. Methods: A retrospective multisite cohort study of IPC and NSQIP superficial, deep, and organ-space THR/TKR SSI data collected 30 days postoperatively from September 1, 2015, to March 31, 2018 was undertaken. To identify patients with procedures captured by both IPC and NSQIP, data were cleaned, duplicates removed, and patients matched 1:1 using year of birth, procedure facility, type, side, date, and time. Positive and negative agreement were assessed, and the Cohen κ values were calculated. The definitions and data capture methods used by both IPC and NSQIP were also compared. Results: There were 7,549 IPC and 2,037 NSQIP patients, respectively, with 1,798 matched patients: IPC (23.8%) and NSQIP (88.3%). Moreover, 17 SSIs were identified by both IPC and NSQIP, including 9 superficial and 8 complex by IPC and 6 superficial and 11 complex by NSQIP. Also, 7 SSIs were identified only by IPC, of which 5 were superficial, and 36 SSIs were identified only by NSQIP, of which 28 were superficial (positive agreement, 0.44; negative agreement, 0.99; κ = .43). Excluding superficial SSIs, 7 SSIs were identified by both IPC and NSQIP; 3 were identified only by IPC; and 12 were identified only by NSQIP (positive agreement, 0.48; negative agreement, 1.00; κ = 0.48). Conclusions: THR/TKR SSI rates reported by IPC and NSQIP were not comparable in this matched dataset. NSQIP identifies more superficial SSIs. Variations in data capture methods and definitions accounted for most of the discordance. Both surveillance systems are critically involved with improving patient outcomes following surgery. However, stakeholders need to be aware of these variations, and education should be provided to facilitate an understanding of the differences and their interpretation. Future work should explore other surgical procedures and larger data sets.Funding: NoneDisclosures: None
Developing a Clinically Informed Compartmental Mathematical Model of Pediatric Norovirus Transmission—A Feasibility Study
Authors: O’Callaghan Kevin , Privette Grayson , Handy Lori , Sammons Julia , Levy Michael 
Published: November 2020

Background: Norovirus causes a significant disease burden of 20 million cases per year in the United States. Hospitals and long-term care facilities constitute the most commonly reported settings for noroviral outbreaks and clusters and thus represent a critically important site for prevention. Our institutional surveillance and response system identified 10–14 clusters or outbreaks of gastrointestinal viral disease per year, predominantly affecting staff. We sought to develop a compartmental mathematical model to examine the potential efficacy of various infection control practices in the management of noroviral clusters. Methods: We developed a set of parallel compartments representing both patient and staff categories (nursing, nurse assistants, etc) involved in a prototypical outbreak, using a 38-bed mixed medium- and high-acuity medical unit as the model basis. A susceptible–exposed–infected–recovered/immune (SEIR) model structure was used (Fig. 1). We conducted interviews with infection preventionists and nursing management to parameterize the model with data on (1) staff-to-patient ratios, (2) staff-patient contact time, (3) staff-staff contact time, (4) spatial distribution of patient assignments, and (5) baseline and intraoutbreak infection prevention practices. With these data, we proceeded to develop submodels, building on the primary model, that examined the effects of additional parallel compartmentalization of granular groups of staff, including resident physicians, environmental services, and clinical nursing assistants. Model parameters for these subanalyses were informed by interviews with clinical experts and review of internal data. Results and Conclusions: An SEIR model was developed that allowed for examination of a modeled outbreak of norovirus and comparison with a known prior outbreak on the same modeled unit for fidelity. Submodeling was performed with more staffing detail, allowing for the addition of further parallel SEIR tracks that delineated more granular staffing patterns. Staff interviews proved critical in the parameterization of these submodels, allowing for a more faithful representation of real-world dynamics. This work, through modification of model parameterization, can be used to assess the efficacy of hypothetical infection control interventions (eg, earlier unit closure, longer staff furlough) in altering transmission dynamics during an outbreak.Funding: NoneDisclosures: None