Classification of reports of patient hospitalization and death: The ETDRS electronic tracking system

1987 ◽  
Vol 8 (3) ◽  
pp. 309
2021 ◽  
Vol 1 (S1) ◽  
pp. s48-s48
Author(s):  
Pragya Dhaubhadel ◽  
Margie Pace ◽  
Trina Augustine ◽  
Seth Hostetler ◽  
Mark Shelly

Background: Significant outbreaks of SARS-CoV-2 infections have occurred in healthcare personnel (HCP). We used an electronic tracking system (ETS) as a tool to link staff cases of COVID-19 in place and time during a COVID-19 outbreak in a community hospital. Methods: We identified SARS-CoV-2 infection cases through surveillance, case investigation and contact tracing, and voluntary testing. For those wearing ETS badges (Centrak), data were reviewed for places occupied by the personnel during their incubation and infectious windows. Contacts beyond 15 minutes in the same location were considered close contacts. Results: Over 6 weeks (August 10–September 14, 2020), 35 HCPs tested positive for SARS-CoV-2 by NAAT testing. In total, 18 nurses and aides were clustered on 1 hospital unit, 7 cases occurred among respiratory therapists that visited that unit, and 10 occurred in other departments. Overall, 17 individuals wore ETS badges as part of hand hygiene monitoring. ETS data established potential transmission opportunities in 17 instances, all but 2 before symptom onset or positive test result. Contacts were most often (10 of 17) in common work areas (nursing stations), with a median time of 45 minutes (IQR, 21–137). Contacts occurred within and between departments. A few COVID-19 patients were cared for in this location at the time of the outbreak. However, we did not detect HCP-to-patient nor patient-to-HCP transmission. Conclusions: Significant HCP-to-HCP transmission occurred during this outbreak based on ETS location. These events often occurred in shared work areas such as the nursing station in addition to break areas noted in other reports. ETS systems, installed for other purposes, can serve to reinforce standard epidemiology.Funding: NoDisclosures: None


2021 ◽  
Vol 108 (Supplement_9) ◽  
Author(s):  
Scarlet Nazarian ◽  
Ioannis Gkouzionis ◽  
Michal Kawka ◽  
Nisha Patel ◽  
Ara Darzi ◽  
...  

Abstract Background Diffuse reflectance spectroscopy (DRS) is a technique that allows discrimination of normal and abnormal tissue based on spectral data. It is a promising technique for cancer margin assessment. However, application in a clinical setting is limited by the inability of DRS to mark the tissue that has been scanned and its lack of continuous real-time spectral measurements. This aim of this study was to develop a real-time tracking system to enable localisation of the tip of a handheld DRS probe to aid classification of tumour and non-tumour tissue. Methods A coloured marker was attached to the DRS fibre probe and was detected using colour segmentation. A Kalman filter was used to estimate the probe’s tip position during scanning of the tissue specimen. In this way, the system was robust to partial occlusion allowing real-time detection and tracking. Supervised classification algorithms were used for the discrimination between tumour and non-tumour tissue, and evaluated in terms of overall accuracy, sensitivity, specificity, and the area under the curve (AUC). A live augmented view with all the tracked and classified optical biopsy sites were presented, providing visual feedback to the surgeons. Results A green coloured marker was successfully used to track the DRS probe. The measured root mean square error of probe tip tracking was 1.18±0.58mm and 1.05±0.28mm for the X and Y directions, respectively, whilst the maximum measured error was 1.76mm. Overall, 47 distinct sets of tumour and non-tumour tissue data were recorded through real-time tracking of ex vivo oesophageal and gastric tissue. The overall diagnostic accuracy of the system to classify tumour and non-tumour tissue in real-time was 94% for stomach and 96% for the oesophagus. Conclusions We have been able to successfully develop a real-time tracking system for a DRS probe when used on stomach and oesophageal tissue for tumour detection, and the accuracy derived demonstrates the strength and clinical value of the technique. The method allows real-time tracking and classification with short data acquisition time to aid margin assessment in cancer resection surgery.


2013 ◽  
Vol 21 (3) ◽  
pp. 782-789 ◽  
Author(s):  
徐涛 XU Tao ◽  
李博 LI Bo ◽  
刘廷霞 LIU Ting-xia ◽  
薛乐堂 XUE Le-tang ◽  
陈涛 Chen Tao

Author(s):  
B. Watson ◽  
N. Danq ◽  
I. Davis ◽  
D. Edwards ◽  
A. Rudge ◽  
...  

2013 ◽  
Vol 21 (7) ◽  
pp. 1818-1824
Author(s):  
郭宁 GUO Ning ◽  
吕俊伟 LV Jun-wei ◽  
邓江生 DENG Jiang-sheng

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