Hospital-acquired COVID-19: a New Dilemma for the Infection Control Practitioner

BackgroundPreliminary reports show that nosocomial SARS-CoV-2 infection is not associated with increased mortality compared with community acquired infection.MethodsRetrospective comparison of COVID-19 adult patients who were classified according to probable time of acquisition of SARS-CoV-2 and symptom onset. Data from hospitalized patients that were hospitalized in non-COVID-19 areas were reviewed.All patients were classified as community-acquired/Community-onset (CA-CO), Community- acquired/hospital-onset (CA-HO) and Hospital-acquired/Hospital-onset (HA-HO) cases. All patients without respiratory symptoms were tested on day one and if negative, hospitalized in non-COVID-19 areas.Results We identified 59 patients that fulfilled the definition of CA-HO or HA-HO COVID-19. Patients in the CA-CO group were less likely to have multiple comorbidities than the patients in the CA-HO and HA-HO groups. Mortality was lower in the CA-CO group (21.8%) compared to the other groups, although it did not reach statistical significance. DiscussionWe identified 9 clusters of HA-HO cases arising from multiple-bed rooms from the non-COVID-19 areas. There was no significant difference for HA-HO COVID-19 between patients placed in a common-room bed compared to patients placed on single bed rooms (p=.19). Nevertheless, the RR for HA-HO COVID-19 was 105 (95% CI 62.9 to 177.6) for patients treated in a common-room allocating another COVID-19-detected patient within the immediate 24 h time frame (P=<0.01).ConclusionHospital-acquired COVID-19 is newly described and poses a challenge for infection control. We identified small clusters related to multiple-bed rooms from non-COVID-19 hospitalization wards and propose a simple time-based classification for hospital surveillance and isolation precautions.

How to Convince People and Get Adherence to Hand Hygiene Practices? The Success of Ozires, the Humanoid Robot!

Background: Our team has been fighting nosocomial infections since 1991. During our journey, we often ask why people do not wash their hands! Semmelweiss discovered in the 1840s that handwashing prevented deaths from puerperal sepsis, but we still need to convince healthcare workers about hand hygiene. One answer is that washing hands is an unsophisticated gesture, without any technology, so people just do not do it. How can we improve compliance with hand hygiene? We imagined a robot in our team to remind people to wash their hands. Then, in 2016 we met Meccanoid, a US$200 toy robot: a 4-foot-tall programmable humanoid robot with voice recognition capabilities. We made adaptions in the robot (mini-projector + audio amplifier + alcohol dispenser + spy camera), and we gave him a name (Ozires) and a purpose: He became a professor who teaches healthcare workers how, when, and why wash their hands! Here, we describe the multimodal strategy centered around Ozires. Methods: The multimodal strategy consists of 7 key elements: (1) the robot, accompanied by a infection control practitioner, performs audio and video lectures about hand hygiene techniques, motivational videos, data feedback; (2) the robot’s wood copies with sound alert with motion detector for hand hygiene are spread out in the whole hospital; (3) fridge magnet with robot prints (gifts for patients and healthcare professionals); (4) app for hand hygiene monitoring (Hands Clean); (5) adherence rates by professional category and individual feedback; (6) patient empowerment for hand hygiene; and (7) sound alert for hand hygiene in the patient room’s door. Results: After the insertion of Ozires in 3 ICUs of hospital A (pilot study), the hand hygiene (HH) rate increased from ~36%, between January and July 2016, to ~68% between August 2016 and October 2019. At hospital B, Ozires started his lectures in May 2018, throughout the hospital. Hand hygiene adherence increased from 23% between July and December 2017 to 60% between June 2018 and October 2019. In the 3 months before this multimodal strategy was implemented in hospital C (June–August 2019), and the mean rate of hand hygiene was 65%. With the robot, the hand hygiene rate increased to 94% (September–October 2019). Conclusions: The multimodal strategy centered around the robot Ozires works! Hand hygiene compliance increased significantly after the interventions. People listen the robot much more attentively than to their human colleagues, and healthcare worker behavior changed! We need to go further improve the program, but it is sustainable. Finally, we succeeded in convincing people to improve their hand hygiene practices.Funding: NoneDisclosures: None

Deep Learning versus Conventional Machine Learning for Detection of Healthcare-Associated Infections in French Clinical Narratives

Objective The objective of this article was to compare the performances of health care-associated infection (HAI) detection between deep learning and conventional machine learning (ML) methods in French medical reports. Methods The corpus consisted in different types of medical reports (discharge summaries, surgery reports, consultation reports, etc.). A total of 1,531 medical text documents were extracted and deidentified in three French university hospitals. Each of them was labeled as presence (1) or absence (0) of HAI. We started by normalizing the records using a list of preprocessing techniques. We calculated an overall performance metric, the F1 Score, to compare a deep learning method (convolutional neural network [CNN]) with the most popular conventional ML models (Bernoulli and multi-naïve Bayes, k-nearest neighbors, logistic regression, random forests, extra-trees, gradient boosting, support vector machines). We applied the hyperparameter Bayesian optimization for each model based on its HAI identification performances. We included the set of text representation as an additional hyperparameter for each model, using four different text representations (bag of words, term frequency–inverse document frequency, word2vec, and Glove). Results CNN outperforms all other conventional ML algorithms for HAI classification. The best F1 Score of 97.7% ± 3.6% and best area under the curve score of 99.8% ± 0.41% were achieved when CNN was directly applied to the processed clinical notes without a pretrained word2vec embedding. Through receiver operating characteristic curve analysis, we could achieve a good balance between false notifications (with a specificity equal to 0.937) and system detection capability (with a sensitivity equal to 0.962) using the Youden's index reference. Conclusions The main drawback of CNNs is their opacity. To address this issue, we investigated CNN inner layers' activation values to visualize the most meaningful phrases in a document. This method could be used to build a phrase-based medical assistant algorithm to help the infection control practitioner to select relevant medical records. Our study demonstrated that deep learning approach outperforms other classification learning algorithms for automatically identifying HAIs in medical reports.

2018 AAHA Infection Control, Prevention, and Biosecurity Guidelines*

ABSTRACT A veterinary team’s best work can be undone by a breach in infection control, prevention, and biosecurity (ICPB). Such a breach, in the practice or home-care setting, can lead to medical, social, and financial impacts on patients, clients, and staff, as well as damage the reputation of the hospital. To mitigate these negative outcomes, the AAHA ICPB Guidelines Task Force believes that hospital teams should improve upon their current efforts by limiting pathogen exposure from entering or being transmitted throughout the hospital population and using surveillance methods to detect any new entry of a pathogen into the practice. To support these recommendations, these practice-oriented guidelines include step-by-step instructions to upgrade ICPB efforts in any hospital, including recommendations on the following: establishing an infection control practitioner to coordinate and implement the ICPB program; developing evidence-based standard operating procedures related to tasks performed frequently by the veterinary team (hand hygiene, cleaning and disinfection, phone triage, etc.); assessing the facility’s ICPB strengths and areas of improvement; creating a staff education and training plan; cataloging client education material specific for use in the practice; implementing a surveillance program; and maintaining a compliance evaluation program. Practices with few or no ICPB protocols should be encouraged to take small steps. Creating visible evidence that these protocols are consistently implemented within the hospital will invariably strengthen the loyalties of clients to the hospital as well as deepen the pride the staff have in their roles, both of which are the basis of successful veterinary practice.

Hand hygiene compliance monitoring in anaesthetics: Feasibility and validity

Background: Hand hygiene compliance scores in the anaesthetic department of an acute NHS hospital were persistently low. Aims: To determine the feasibility and validity of regular accurate measurement of HHC in anaesthetics and understand the context of care delivery, barriers and opportunities to improve compliance. Methods: The hand hygiene compliance of one anaesthetist was observed and noted by a senior infection control practitioner (ICP). This was compared to the World Health Organization five moments of hand hygiene and the organisation hand hygiene tool. Findings: In one sequence of 55 min, there were approximately 58 hand hygiene opportunities. The hand hygiene compliance rate was 16%. The frequency and speed of actions in certain periods of care delivery made compliance measurement difficult and potentially unreliable. During several activities, taking time to apply alcohol gel or wash hands would have put the patients at significant risk. Discussion: We concluded that hand hygiene compliance monitoring by direct observation was invalid and unreliable in this specialty. It is important that hand hygiene compliance is optimal in anaesthetics particularly before patient contact. Interventions which reduce environmental and patient contamination, such as cleaning the patient and environment, could ensure anaesthetists encounter fewer micro-organisms in this specialty.

An Automated Algorithm to Preselect Patients to Be Assessed Individually in Point Prevalence Surveys for Hospital-Acquired Infections in Surgery

In this pilot study, we evaluate an algorithm that uses predictive clinical and laboratory parameters to differentiate between patients with hospital-acquired infection (HAI) and patients without HAI. Seventy-four percent of the studied population of surgical patients could be reliably (negative predictive value of 98%) excluded from detailed assessment by the infection control practitioner.Infect Control Hosp Epidemiol 2014;35(7):886-887