We propose the use of a machine learning algorithm to improve possible COVID-19 case identification more quickly using a mobile phone–based web survey. This method could reduce the spread of the virus in susceptible populations under quarantine.

Residents living in long-term care facilities (LTCFs) are at high risk of contracting healthcare-associated infections (HAIs). The unique operational and cultural characteristics of LTCFs and the currently evolving models of healthcare delivery in Scotland create great challenges for infection prevention and control (IPC). Existing literature that discusses the challenges of infection control in LTCFs focuses on operational factors within a facility and does not explore the challenges associated with higher levels of management and the lack of evidence to support IPC practices in this setting.1-7 Here, we provide a broader view of challenges faced by LTCFs in the context of the current health and social care models in Scotland. Many of these challenges are also faced in the rest of the United Kingdom and internationally.

Hospital-acquired infections remain a common cause of morbidity and mortality despite advances in infection prevention through use of bundles, environmental cleaning, antimicrobial stewardship, and other best practices. Current prevention strategies and further hospital-acquired infection reduction are limited by lack of recognition of the role that host–microbe interactions play in susceptibility and by the inability to analyze multiple risk factors in real time to accurately predict the likelihood of a hospital-acquired infection before it occurs and to inform medical decision making. Herein, we examine the value of incorporating the damage-response framework and host attributes that determine susceptibility to infectious diseases known by the acronym MISTEACHING (ie, microbiome, immunity, sex, temperature, environment, age, chance, history, inoculum, nutrition, genetics) into infection prevention strategies using machine learning to drive decision support and patient-specific interventions.

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: None

Disclosures: Marco Bommarito, 3M (salary)

Cutaneous antisepsis with chlorhexidine or povidone-iodine, usually with alcohol, has been extensively studied. This review of published studies reveals that sequential use of povidone-iodine and chlorhexidine leads to a greater reduction in the bioburden of aerobic and anaerobic bacteria on the skin, lower risk of intravascular catheter colonization, and lower risk of surgical site infection compared to use of either agent alone. As such, sequential use of cutaneous antiseptic agents may further reduce risk of surgical site infections, as well as infections associated with insertion of transdermal devices such as nephrostomy tubes, left-ventricular assistance devices, and intravascular catheters.

Healthcare employees were tested for antibodies against severe acute respiratory coronavirus virus 2 (SARS-CoV-2). Among 734 employees, the prevalence of SARS-CoV-2 antibodies was 1.6%. Employees with heavy coronavirus disease 2019 (COVID-19) exposure had similar antibody prevalence as those with limited or no exposure. Guidelines for PPE use seem effective for preventing COVID-19 infection in healthcare workers.

Our stewardship team evaluated 19 months of discharge antibiotic prescriptions to determine prescribing appropriateness and to characterize the interventions made. Intervention occurred in 9.7% of patients, with a 58% acceptance rate. Most interventions were educational (antibiotic course was complete at time of intervention). Discharge antibiotic review is a potential stewardship tool.

Classical stewardship efforts have targeted immunocompetent patients; however, appropriate use of antimicrobials in the immunocompromised host has become a target of interest. Cytomegalovirus (CMV) infection is one of the most common and significant complications after solid-organ transplant (SOT). The treatment of CMV requires a dual approach of antiviral drug therapy and reduction of immunosuppression for optimal outcomes. This dual approach to CMV management increases complexity and requires individualization of therapy to balance antiviral efficacy with the risk of allograft rejection. In this review, we focus on the development and implementation of CMV stewardship initiatives, as a component of antimicrobial stewardship in the immunocompromised host, to optimize the management of prevention and treatment of CMV in SOT recipients. These initiatives have the potential not only to improve judicious use of antivirals and prevent resistance but also to improve patient and graft survival given the interconnection between CMV infection and allograft function.

Background: Simulations based on models of healthcare worker (HCW) mobility and contact patterns with patients provide a key tool for understanding spread of healthcare-acquired infections (HAIs). However, simulations suffer from lack of accurate model parameters. This research uses Microsoft Kinect cameras placed in a patient room in the medical intensive care unit (MICU) at the University of Iowa Hospitals and Clinics (UIHC) to obtain reliable distributions of HCW visit length and time spent by HCWs near a patient. These data can inform modeling efforts for understanding HAI spread. Methods: Three Kinect cameras (left, right, and door cameras) were placed in a patient room to track the human body (ie, left/right hands and head) at 30 frames per second. The results reported here are based on 7 randomly selected days from a total of 308 observation days. Each tracked body may have multiple raw segments over the 2 camera regions, which we “stitch” up by matching features (eg, direction, velocity, etc), to obtain complete trajectories. Due to camera noise, in a substantial fraction of the frames bodies display unnatural characteristics including frequent and rapid directional and velocity change. We use unsupervised learning techniques to identify such “ghost” frames and we remove from our analysis bodies that have 20% or more “ghost” frames. Results: The heat map of hand positions (Fig. 1) shows that high-frequency locations are clustered around the bed and more to the patient’s right in accordance with the general medical practice of performing patient exams from their right. HCW visit frequency per hour (mean, 6.952; SD, 2.855) has 2 peaks, 1 during morning shift and 1 during the afternoon shift, with a distinct decrease after midnight. Figure 2 shows visit length (in minutes) distribution (mean, 1.570; SD, 2.679) being dominated by “check in visits” of <30 seconds. HCWs do not spend much time at touching distance from patients during short-length visits, and the fraction of time spent near the patient’s bed seems to increase with visit length up to a point. Conclusions: Using fine-grained data, this research extracts distributions of these critical parameters of HCW–patient interactions: (1) HCW visit length, (2) HCW visit frequency as a function of time of day, and (3) time spent by HCW within touching distance of patient as a function of visit length. To the best of our knowledge, we provide the first reliable estimates of these parameters.

Funding: None

Disclosures: None

Antimicrobial stewardship improves patient care and reduces antimicrobial resistance, inappropriate use, and adverse outcomes. Despite high-profile mandates for antimicrobial stewardship programs across the healthcare continuum, descriptive data, and recommendations for dedicated resources, including appropriate physician, pharmacist, data analytics, and administrative staffing support, are not robust. This review summarizes the current literature on antimicrobial stewardship staffing and calls for the development of minimum staffing recommendations.

Reducing severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infections among healthcare workers is critical. We ran Monte Carlo simulations modeling the spread of SARS-CoV-2 in non–COVID-19 wards, and we found that longer nursing shifts and scheduling designs in which teams of nurses and doctors co-rotate no more frequently than every 3 days can lead to fewer infections.

Data visualization refers to the techniques used to communicate information by encoding it as visual objects (eg, points, lines, or bars) contained in graphics. The recent acceleration in informatics technology has made it possible to obtain and process large amounts of data. Although data visualization can provide insights from large datasets, it can also help simplify messaging, making information more accessible for healthcare stakeholders. The field of data visualization is constantly evolving, and new techniques are frequently being created. However, evidence regarding the best way to visualize data in the fields of infection prevention and antimicrobial stewardship is limited. We provide an overview of data visualization theory and history, as well as recommendations for creating graphs for infection prevention and antimicrobial stewardship.