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10.1001/jamanetworkopen.2021.4347 http://scihub22266oqcxt.onion/10.1001/jamanetworkopen.2021.4347 33822066!8025111!33822066     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       JAMA+Netw+Open 2021 ; 4 (4): e214347 Nephropedia Template TP {{tp|p=33822066|t=2021. Development of COVIDVax Model to Estimate the Risk of SARS-CoV-2-Related Death Among 7 6 Million US Veterans for Use in Vaccination Prioritization |pdf=|usr=}}{{33822066}} gab.com Text Development of COVIDVax Model to Estimate the Risk of SARS-CoV-2-Related Death Among 7 6 Million US Veterans for Use in Vaccination Prioritization JO: JAMA Netw Open http://www.kidney.de/pget.php?&tw=1&pmid=33822066 #FOAvip IMPORTANCE: A strategy that prioritizes individuals for SARS-CoV-2 vaccination according to their risk of SARS-CoV-2-related mortality would help minimize deaths during vaccine rollout. OBJECTIVE: To develop a model that estimates the risk of SARS-CoV-2-related mortality among all enrollees of the US Department of Veterans Affairs (VA) health care system. DESIGN, SETTING, AND PARTICIPANTS: This prognostic study used data from 7?635?064 individuals enrolled in the VA health care system as of May 21, 2020, to develop and internally validate a logistic regression model (COVIDVax) that predicted SARS-CoV-2-related death (n = 2422) during the observation period (May 21 to November 2, 2020) using baseline characteristics known to be associated with SARS-CoV-2-related mortality, extracted from the VA electronic health records (EHRs). The cohort was split into a training period (May 21 to September 30) and testing period (October 1 to November 2). MAIN OUTCOMES AND MEASURES: SARS-CoV-2-related death, defined as death within 30 days of testing positive for SARS-CoV-2. VA EHR data streams were imported on a data integration platform to demonstrate that the model could be executed in real-time to produce dashboards with risk scores for all current VA enrollees. RESULTS: Of 7?635?064 individuals, the mean (SD) age was 66.2 (13.8) years, and most were men (7?051?912 [92.4%]) and White individuals (4?887?338 [64.0%]), with 1?116?435 (14.6%) Black individuals and 399?634 (5.2%) Hispanic individuals. From a starting pool of 16 potential predictors, 10 were included in the final COVIDVax model, as follows: sex, age, race, ethnicity, body mass index, Charlson Comorbidity Index, diabetes, chronic kidney disease, congestive heart failure, and Care Assessment Need score. The model exhibited excellent discrimination with area under the receiver operating characteristic curve (AUROC) of 85.3% (95% CI, 84.6%-86.1%), superior to the AUROC of using age alone to stratify risk (72.6%; 95% CI, 71.6%-73.6%). Assuming vaccination is 90% effective at preventing SARS-CoV-2-related death, using this model to prioritize vaccination was estimated to prevent 63.5% of deaths that would occur by the time 50% of VA enrollees are vaccinated, significantly higher than the estimate for prioritizing vaccination based on age (45.6%) or the US Centers for Disease Control and Prevention phases of vaccine allocation (41.1%). CONCLUSIONS AND RELEVANCE: In this prognostic study of all VA enrollees, prioritizing vaccination based on the COVIDVax model was estimated to prevent a large proportion of deaths expected to occur during vaccine rollout before sufficient herd immunity is achieved. Twit Text FOAVip Development of COVIDVax Model to Estimate the Risk of SARS-CoV-2-Related Death Among 7 6 Million US Veterans for Use in Vaccination Prioritization ????JAMA Netw Open http://www.kidney.de/pget.php?&tw=1&pmid=33822066 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33822066 #FOAvip English Wikipedia <ref>{{Cite pmid|33822066}}</ref> Development of COVIDVax Model to Estimate the Risk of SARS-CoV-2-Related Death Among 7 6 Million US Veterans for Use in Vaccination Prioritization #MMPMID33822066 Ioannou GN; Green P; Fan VS; Dominitz JA; O'Hare AM; Backus LI; Locke E; Eastment MC; Osborne TF; Ioannou NG; Berry K JAMA Netw Open 2021[Apr]; 4 (4): e214347 PMID33822066show ga IMPORTANCE: A strategy that prioritizes individuals for SARS-CoV-2 vaccination according to their risk of SARS-CoV-2-related mortality would help minimize deaths during vaccine rollout. OBJECTIVE: To develop a model that estimates the risk of SARS-CoV-2-related mortality among all enrollees of the US Department of Veterans Affairs (VA) health care system. DESIGN, SETTING, AND PARTICIPANTS: This prognostic study used data from 7?635?064 individuals enrolled in the VA health care system as of May 21, 2020, to develop and internally validate a logistic regression model (COVIDVax) that predicted SARS-CoV-2-related death (n = 2422) during the observation period (May 21 to November 2, 2020) using baseline characteristics known to be associated with SARS-CoV-2-related mortality, extracted from the VA electronic health records (EHRs). The cohort was split into a training period (May 21 to September 30) and testing period (October 1 to November 2). MAIN OUTCOMES AND MEASURES: SARS-CoV-2-related death, defined as death within 30 days of testing positive for SARS-CoV-2. VA EHR data streams were imported on a data integration platform to demonstrate that the model could be executed in real-time to produce dashboards with risk scores for all current VA enrollees. RESULTS: Of 7?635?064 individuals, the mean (SD) age was 66.2 (13.8) years, and most were men (7?051?912 [92.4%]) and White individuals (4?887?338 [64.0%]), with 1?116?435 (14.6%) Black individuals and 399?634 (5.2%) Hispanic individuals. From a starting pool of 16 potential predictors, 10 were included in the final COVIDVax model, as follows: sex, age, race, ethnicity, body mass index, Charlson Comorbidity Index, diabetes, chronic kidney disease, congestive heart failure, and Care Assessment Need score. The model exhibited excellent discrimination with area under the receiver operating characteristic curve (AUROC) of 85.3% (95% CI, 84.6%-86.1%), superior to the AUROC of using age alone to stratify risk (72.6%; 95% CI, 71.6%-73.6%). Assuming vaccination is 90% effective at preventing SARS-CoV-2-related death, using this model to prioritize vaccination was estimated to prevent 63.5% of deaths that would occur by the time 50% of VA enrollees are vaccinated, significantly higher than the estimate for prioritizing vaccination based on age (45.6%) or the US Centers for Disease Control and Prevention phases of vaccine allocation (41.1%). CONCLUSIONS AND RELEVANCE: In this prognostic study of all VA enrollees, prioritizing vaccination based on the COVIDVax model was estimated to prevent a large proportion of deaths expected to occur during vaccine rollout before sufficient herd immunity is achieved. |*Mass Vaccination[MESH] |Aged[MESH] |Area Under Curve[MESH] |COVID-19 Vaccines/*therapeutic use[MESH] |COVID-19/*prevention & control[MESH] |Comorbidity[MESH] |Female[MESH] |Health Planning/*methods[MESH] |Health Priorities/*statistics & numerical data[MESH] |Humans[MESH] |Logistic Models[MESH] |Male[MESH] |Middle Aged[MESH] |Prognosis[MESH] |ROC Curve[MESH] |Risk Assessment[MESH] |Risk Factors[MESH] |SARS-CoV-2[MESH] |United States[MESH]Development of COVIDVax Model to Estimate the Risk of SARS-CoV-2-Relat(epmc).pdf DeepDyve Pubget Overpricing