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.

10.1093/ofid/ofaa463 http://scihub22266oqcxt.onion/10.1093/ofid/ofaa463 33204755!7650986!33204755     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Open+Forum+Infect+Dis 2020 ; 7 (11): ofaa463 Nephropedia Template TP {{tp|p=33204755|t=2020. Derivation and Validation of Clinical Prediction Rules for COVID-19 Mortality in Ontario, Canada |pdf=|usr=}}{{33204755}} gab.com Text Derivation and Validation of Clinical Prediction Rules for COVID-19 Mortality in Ontario, Canada JO: Open Forum Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=33204755 #FOAvip BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently causing a high-mortality global pandemic. The clinical spectrum of disease caused by this virus is broad, ranging from asymptomatic infection to organ failure and death. Risk stratification of individuals with coronavirus disease 2019 (COVID-19) is desirable for management, and prioritization for trial enrollment. We developed a prediction rule for COVID-19 mortality in a population-based cohort in Ontario, Canada. METHODS: Data from Ontario's provincial iPHIS system were extracted for the period from January 23 to May 15, 2020. Logistic regression-based prediction rules and a rule derived using a Cox proportional hazards model were developed and validated using split-halves validation. Sensitivity analyses were performed, with varying approaches to missing data. RESULTS: Of 21 922 COVID-19 cases, 1734 with complete data were included in the derivation set; 1796 were included in the validation set. Age and comorbidities (notably diabetes, renal disease, and immune compromise) were strong predictors of mortality. Four point-based prediction rules were derived (base case, smoking excluded, long-term care excluded, and Cox model-based). All displayed excellent discrimination (area under the curve for all rules > 0.92) and calibration (P > .50 by Hosmer-Lemeshow test) in the derivation set. All performed well in the validation set and were robust to varying approaches to replacement of missing variables. CONCLUSIONS: We used a public health case management data system to build and validate 4 accurate, well-calibrated, robust clinical prediction rules for COVID-19 mortality in Ontario, Canada. While these rules need external validation, they may be useful tools for management, risk stratification, and clinical trials. Twit Text FOAVip Derivation and Validation of Clinical Prediction Rules for COVID-19 Mortality in Ontario, Canada ????Open Forum Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=33204755 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33204755 #FOAvip English Wikipedia <ref>{{Cite pmid|33204755}}</ref> Derivation and Validation of Clinical Prediction Rules for COVID-19 Mortality in Ontario, Canada #MMPMID33204755 Fisman DN; Greer AL; Hillmer M; Tuite R Open Forum Infect Dis 2020[Nov]; 7 (11): ofaa463 PMID33204755show ga BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently causing a high-mortality global pandemic. The clinical spectrum of disease caused by this virus is broad, ranging from asymptomatic infection to organ failure and death. Risk stratification of individuals with coronavirus disease 2019 (COVID-19) is desirable for management, and prioritization for trial enrollment. We developed a prediction rule for COVID-19 mortality in a population-based cohort in Ontario, Canada. METHODS: Data from Ontario's provincial iPHIS system were extracted for the period from January 23 to May 15, 2020. Logistic regression-based prediction rules and a rule derived using a Cox proportional hazards model were developed and validated using split-halves validation. Sensitivity analyses were performed, with varying approaches to missing data. RESULTS: Of 21 922 COVID-19 cases, 1734 with complete data were included in the derivation set; 1796 were included in the validation set. Age and comorbidities (notably diabetes, renal disease, and immune compromise) were strong predictors of mortality. Four point-based prediction rules were derived (base case, smoking excluded, long-term care excluded, and Cox model-based). All displayed excellent discrimination (area under the curve for all rules > 0.92) and calibration (P > .50 by Hosmer-Lemeshow test) in the derivation set. All performed well in the validation set and were robust to varying approaches to replacement of missing variables. CONCLUSIONS: We used a public health case management data system to build and validate 4 accurate, well-calibrated, robust clinical prediction rules for COVID-19 mortality in Ontario, Canada. While these rules need external validation, they may be useful tools for management, risk stratification, and clinical trials. äDerivation and Validation of Clinical Prediction Rules for COVID-19 Mo(epmc).pdf DeepDyve Pubget Overpricing