Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.7554/eLife.65828 http://scihub22266oqcxt.onion/10.7554/eLife.65828 34184637!8285103!34184637     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Elife 2021 ; 10 (ä): ä Nephropedia Template TP {{tp|p=34184637|t=2021. Rapid feedback on hospital onset SARS-CoV-2 infections combining epidemiological and sequencing data |pdf=|usr=}}{{34184637}} gab.com Text Rapid feedback on hospital onset SARS-CoV-2 infections combining epidemiological and sequencing data JO: Elife http://www.kidney.de/pget.php?&tw=1&pmid=34184637 #FOAvip BACKGROUND: Rapid identification and investigation of healthcare-associated infections (HCAIs) is important for suppression of SARS-CoV-2, but the infection source for hospital onset COVID-19 infections (HOCIs) cannot always be readily identified based only on epidemiological data. Viral sequencing data provides additional information regarding potential transmission clusters, but the low mutation rate of SARS-CoV-2 can make interpretation using standard phylogenetic methods difficult. METHODS: We developed a novel statistical method and sequence reporting tool (SRT) that combines epidemiological and sequence data in order to provide a rapid assessment of the probability of HCAI among HOCI cases (defined as first positive test >48 hr following admission) and to identify infections that could plausibly constitute outbreak events. The method is designed for prospective use, but was validated using retrospective datasets from hospitals in Glasgow and Sheffield collected February-May 2020. RESULTS: We analysed data from 326 HOCIs. Among HOCIs with time from admission >/=8 days, the SRT algorithm identified close sequence matches from the same ward for 160/244 (65.6%) and in the remainder 68/84 (81.0%) had at least one similar sequence elsewhere in the hospital, resulting in high estimated probabilities of within-ward and within-hospital transmission. For HOCIs with time from admission 3-7 days, the SRT probability of healthcare acquisition was >0.5 in 33/82 (40.2%). CONCLUSIONS: The methodology developed can provide rapid feedback on HOCIs that could be useful for infection prevention and control teams, and warrants further prospective evaluation. The integration of epidemiological and sequence data is important given the low mutation rate of SARS-CoV-2 and its variable incubation period. FUNDING: COG-UK HOCI funded by COG-UK consortium, supported by funding from UK Research and Innovation, National Institute of Health Research and Wellcome Sanger Institute. Twit Text FOAVip Rapid feedback on hospital onset SARS-CoV-2 infections combining epidemiological and sequencing data ????Elife http://www.kidney.de/pget.php?&tw=1&pmid=34184637 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34184637 #FOAvip English Wikipedia <ref>{{Cite pmid|34184637}}</ref> Rapid feedback on hospital onset SARS-CoV-2 infections combining epidemiological and sequencing data #MMPMID34184637 Stirrup O; Hughes J; Parker M; Partridge DG; Shepherd JG; Blackstone J; Coll F; Keeley A; Lindsey BB; Marek A; Peters C; Singer JB; Tamuri A; de Silva TI; Thomson EC; Breuer J Elife 2021[Jun]; 10 (ä): ä PMID34184637show ga BACKGROUND: Rapid identification and investigation of healthcare-associated infections (HCAIs) is important for suppression of SARS-CoV-2, but the infection source for hospital onset COVID-19 infections (HOCIs) cannot always be readily identified based only on epidemiological data. Viral sequencing data provides additional information regarding potential transmission clusters, but the low mutation rate of SARS-CoV-2 can make interpretation using standard phylogenetic methods difficult. METHODS: We developed a novel statistical method and sequence reporting tool (SRT) that combines epidemiological and sequence data in order to provide a rapid assessment of the probability of HCAI among HOCI cases (defined as first positive test >48 hr following admission) and to identify infections that could plausibly constitute outbreak events. The method is designed for prospective use, but was validated using retrospective datasets from hospitals in Glasgow and Sheffield collected February-May 2020. RESULTS: We analysed data from 326 HOCIs. Among HOCIs with time from admission >/=8 days, the SRT algorithm identified close sequence matches from the same ward for 160/244 (65.6%) and in the remainder 68/84 (81.0%) had at least one similar sequence elsewhere in the hospital, resulting in high estimated probabilities of within-ward and within-hospital transmission. For HOCIs with time from admission 3-7 days, the SRT probability of healthcare acquisition was >0.5 in 33/82 (40.2%). CONCLUSIONS: The methodology developed can provide rapid feedback on HOCIs that could be useful for infection prevention and control teams, and warrants further prospective evaluation. The integration of epidemiological and sequence data is important given the low mutation rate of SARS-CoV-2 and its variable incubation period. FUNDING: COG-UK HOCI funded by COG-UK consortium, supported by funding from UK Research and Innovation, National Institute of Health Research and Wellcome Sanger Institute. |COVID-19/*diagnosis/*epidemiology[MESH] |Cross Infection/*diagnosis/*epidemiology[MESH] |Disease Outbreaks/*statistics & numerical data[MESH] |Genome, Viral[MESH] |Hospitals/statistics & numerical data[MESH] |Humans[MESH] |Population Surveillance/*methods[MESH] |Probability[MESH] |Retrospective Studies[MESH] |SARS-CoV-2/*genetics[MESH] |United Kingdom/epidemiology[MESH]Rapid feedback on hospital onset SARS-CoV-2 infections combining epide(epmc).pdf DeepDyve Pubget Overpricing