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10.1136/emermed-2020-210199 http://scihub22266oqcxt.onion/10.1136/emermed-2020-210199 32948623!ä!32948623 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       Emerg+Med+J 2020 ; 37 (10): 630-636 Nephropedia Template TP {{tp|p=32948623|t=2020. Development and implementation of a COVID-19 near real-time traffic light system in an acute hospital setting |pdf=|usr=}}{{32948623}} gab.com Text Development and implementation of a COVID-19 near real-time traffic light system in an acute hospital setting JO: Emerg Med J http://www.kidney.de/pget.php?&tw=1&pmid=32948623 #FOAvip Common causes of death in COVID-19 due to SARS-CoV-2 include thromboembolic disease, cytokine storm and adult respiratory distress syndrome (ARDS). Our aim was to develop a system for early detection of disease pattern in the emergency department (ED) that would enhance opportunities for personalised accelerated care to prevent disease progression. A single Trust's COVID-19 response control command was established, and a reporting team with bioinformaticians was deployed to develop a real-time traffic light system to support clinical and operational teams. An attempt was made to identify predictive elements for thromboembolism, cytokine storm and ARDS based on physiological measurements and blood tests, and to communicate to clinicians managing the patient, initially via single consultants. The input variables were age, sex, and first recorded blood pressure, respiratory rate, temperature, heart rate, indices of oxygenation and C-reactive protein. Early admissions were used to refine the predictors used in the traffic lights. Of 923 consecutive patients who tested COVID-19 positive, 592 (64%) flagged at risk for thromboembolism, 241/923 (26%) for cytokine storm and 361/923 (39%) for ARDS. Thromboembolism and cytokine storm flags were met in the ED for 342 (37.1%) patients. Of the 318 (34.5%) patients receiving thromboembolism flags, 49 (5.3% of all patients) were for suspected thromboembolism, 103 (11.1%) were high-risk and 166 (18.0%) were medium-risk. Of the 89 (9.6%) who received a cytokine storm flag from the ED, 18 (2.0% of all patients) were for suspected cytokine storm, 13 (1.4%) were high-risk and 58 (6.3%) were medium-risk. Males were more likely to receive a specific traffic light flag. In conclusion, ED predictors were used to identify high proportions of COVID-19 admissions at risk of clinical deterioration due to severity of disease, enabling accelerated care targeted to those more likely to benefit. Larger prospective studies are encouraged. Twit Text FOAVip Development and implementation of a COVID-19 near real-time traffic light system in an acute hospital setting ????Emerg Med J http://www.kidney.de/pget.php?&tw=1&pmid=32948623 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32948623 #FOAvip English Wikipedia <ref>{{Cite pmid|32948623}}</ref> Development and implementation of a COVID-19 near real-time traffic light system in an acute hospital setting #MMPMID32948623 Vizcaychipi MP; Shovlin CL; McCarthy A; Howard A; Brown A; Hayes M; Singh S; Christie L; Sisson A; Davies R; Lockie C; Popescu M; Gupta A; Armstrong J; Said H; Peters T; T Keays R Emerg Med J 2020[Oct]; 37 (10): 630-636 PMID32948623show ga Common causes of death in COVID-19 due to SARS-CoV-2 include thromboembolic disease, cytokine storm and adult respiratory distress syndrome (ARDS). Our aim was to develop a system for early detection of disease pattern in the emergency department (ED) that would enhance opportunities for personalised accelerated care to prevent disease progression. A single Trust's COVID-19 response control command was established, and a reporting team with bioinformaticians was deployed to develop a real-time traffic light system to support clinical and operational teams. An attempt was made to identify predictive elements for thromboembolism, cytokine storm and ARDS based on physiological measurements and blood tests, and to communicate to clinicians managing the patient, initially via single consultants. The input variables were age, sex, and first recorded blood pressure, respiratory rate, temperature, heart rate, indices of oxygenation and C-reactive protein. Early admissions were used to refine the predictors used in the traffic lights. Of 923 consecutive patients who tested COVID-19 positive, 592 (64%) flagged at risk for thromboembolism, 241/923 (26%) for cytokine storm and 361/923 (39%) for ARDS. Thromboembolism and cytokine storm flags were met in the ED for 342 (37.1%) patients. Of the 318 (34.5%) patients receiving thromboembolism flags, 49 (5.3% of all patients) were for suspected thromboembolism, 103 (11.1%) were high-risk and 166 (18.0%) were medium-risk. Of the 89 (9.6%) who received a cytokine storm flag from the ED, 18 (2.0% of all patients) were for suspected cytokine storm, 13 (1.4%) were high-risk and 58 (6.3%) were medium-risk. Males were more likely to receive a specific traffic light flag. In conclusion, ED predictors were used to identify high proportions of COVID-19 admissions at risk of clinical deterioration due to severity of disease, enabling accelerated care targeted to those more likely to benefit. Larger prospective studies are encouraged. |Adult[MESH] |Age Factors[MESH] |Aged[MESH] |COVID-19[MESH] |Coronavirus Infections/diagnosis/epidemiology/*therapy[MESH] |Disease Progression[MESH] |Emergency Medical Tags/*trends[MESH] |Emergency Service, Hospital/*statistics & numerical data[MESH] |Female[MESH] |Hospital Mortality/*trends[MESH] |Hospitals, University[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Patient Care Team/*organization & administration[MESH] |Patient Selection[MESH] |Pneumonia, Viral/diagnosis/epidemiology/*therapy[MESH] |Precision Medicine/statistics & numerical data[MESH] |Risk Assessment[MESH] |Severity of Illness Index[MESH] |Sex Factors[MESH] |Thromboembolism/*diagnosis/epidemiology/therapy[MESH]Development and implementation of a COVID-19 near real-time traffic li(epmc).pdf DeepDyve Pubget Overpricing