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10.21037/atm-20-4977 http://scihub22266oqcxt.onion/10.21037/atm-20-4977 33437784!7791231!33437784     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       Ann+Transl+Med 2020 ; 8 (23): 1585 Nephropedia Template TP {{tp|p=33437784|t=2020. A predictive model for respiratory distress in patients with COVID-19: a retrospective study |pdf=|usr=}}{{33437784}} gab.com Text A predictive model for respiratory distress in patients with COVID-19: a retrospective study JO: Ann Transl Med http://www.kidney.de/pget.php?&tw=1&pmid=33437784 #FOAvip BACKGROUND: Coronavirus disease 2019 (COVID-19), associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global public health crisis. We retrospectively evaluated 863 hospitalized patients with COVID-19 infection, designated IWCH-COVID-19. METHODS: We built a successful predictive model after investigating the risk factors to predict respiratory distress within 30 days of admission. These variables were analyzed using Kaplan-Meier and Cox proportional hazards (PHs) analyses. Hazard ratios (HRs) and performance of the final model were determined. RESULTS: Neutrophil count >6.3x10(9)/L, D-dimer level >/=1.00 mg/L, and temperature >/=37.3 degrees C at admission showed significant positive association with the outcome of respiratory distress in the final model. Complement C3 (C3) of 0.9-1.8 g/L, platelet count >350x10(9)/L, and platelet count of 125-350x10(9)/L showed a significant negative association with outcomes of respiratory distress in the final model. The final model had a C statistic of 0.891 (0.867-0.915), an Akaike's information criterion (AIC) of 567.65, and a bootstrap confidence interval (CI) of 0.866 (0.842-0.89). This five-factor model could help in early allocation of medical resources. CONCLUSIONS: The predictive model based on the five factors obtained at admission can be applied for calculating the risk of respiratory distress and classifying patients at an early stage. Accordingly, high-risk patients can receive timely and effective treatment, and health resources can be allocated effectively. Twit Text FOAVip A predictive model for respiratory distress in patients with COVID-19: a retrospective study ????Ann Transl Med http://www.kidney.de/pget.php?&tw=1&pmid=33437784 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33437784 #FOAvip English Wikipedia <ref>{{Cite pmid|33437784}}</ref> A predictive model for respiratory distress in patients with COVID-19: a retrospective study #MMPMID33437784 Zhang X; Wang W; Wan C; Cheng G; Yin Y; Cao K; Zhang X; Wang Z; Miao S; Yu Y; Hu J; Huang R; Ge Y; Chen Y; Liu Y Ann Transl Med 2020[Dec]; 8 (23): 1585 PMID33437784show ga BACKGROUND: Coronavirus disease 2019 (COVID-19), associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global public health crisis. We retrospectively evaluated 863 hospitalized patients with COVID-19 infection, designated IWCH-COVID-19. METHODS: We built a successful predictive model after investigating the risk factors to predict respiratory distress within 30 days of admission. These variables were analyzed using Kaplan-Meier and Cox proportional hazards (PHs) analyses. Hazard ratios (HRs) and performance of the final model were determined. RESULTS: Neutrophil count >6.3x10(9)/L, D-dimer level >/=1.00 mg/L, and temperature >/=37.3 degrees C at admission showed significant positive association with the outcome of respiratory distress in the final model. Complement C3 (C3) of 0.9-1.8 g/L, platelet count >350x10(9)/L, and platelet count of 125-350x10(9)/L showed a significant negative association with outcomes of respiratory distress in the final model. The final model had a C statistic of 0.891 (0.867-0.915), an Akaike's information criterion (AIC) of 567.65, and a bootstrap confidence interval (CI) of 0.866 (0.842-0.89). This five-factor model could help in early allocation of medical resources. CONCLUSIONS: The predictive model based on the five factors obtained at admission can be applied for calculating the risk of respiratory distress and classifying patients at an early stage. Accordingly, high-risk patients can receive timely and effective treatment, and health resources can be allocated effectively. äA predictive model for respiratory distress in patients with COVID-19:(epmc).pdf DeepDyve Pubget Overpricing