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10.1186/s12879-021-06369-0 http://scihub22266oqcxt.onion/10.1186/s12879-021-06369-0 34238232!8264491!34238232     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMC+Infect+Dis 2021 ; 21 (1): 663 Nephropedia Template TP {{tp|p=34238232|t=2021. Predictors of mortality in patients with coronavirus disease 2019: a systematic review and meta-analysis |pdf=|usr=}}{{34238232}} gab.com Text Predictors of mortality in patients with coronavirus disease 2019: a systematic review and meta-analysis JO: BMC Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=34238232 #FOAvip BACKGROUND: Coronavirus disease 2019 (COVID-19) is associated with a high mortality rate, especially in patients with severe illness. We conducted a systematic review and meta-analysis to assess the potential predictors of mortality in patients with COVID-19. METHODS: PubMed, EMBASE, the Cochrane Library, and three electronic Chinese databases were searched from December 1, 2019 to April 29, 2020. Eligible studies reporting potential predictors of mortality in patients with COVID-19 were identified. Unadjusted prognostic effect estimates were pooled using the random-effects model if data from at least two studies were available. Adjusted prognostic effect estimates were presented by qualitative analysis. RESULTS: Thirty-six observational studies were identified, of which 27 were included in the meta-analysis. A total of 106 potential risk factors were tested, and the following important predictors were associated with mortality: advanced age, male sex, current smoking status, preexisting comorbidities (especially chronic kidney, respiratory, and cardio-cerebrovascular diseases), symptoms of dyspnea, complications during hospitalization, corticosteroid therapy and a severe condition. Additionally, a series of abnormal laboratory biomarkers of hematologic parameters, hepatorenal function, inflammation, coagulation, and cardiovascular injury were also associated with fatal outcome. CONCLUSION: We identified predictors of mortality in patients with COVID-19. These findings could help healthcare providers take appropriate measures and improve clinical outcomes in such patients. Twit Text FOAVip Predictors of mortality in patients with coronavirus disease 2019: a systematic review and meta-analysis ????BMC Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=34238232 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34238232 #FOAvip English Wikipedia <ref>{{Cite pmid|34238232}}</ref> Predictors of mortality in patients with coronavirus disease 2019: a systematic review and meta-analysis #MMPMID34238232 Shi C; Wang L; Ye J; Gu Z; Wang S; Xia J; Xie Y; Li Q; Xu R; Lin N BMC Infect Dis 2021[Jul]; 21 (1): 663 PMID34238232show ga BACKGROUND: Coronavirus disease 2019 (COVID-19) is associated with a high mortality rate, especially in patients with severe illness. We conducted a systematic review and meta-analysis to assess the potential predictors of mortality in patients with COVID-19. METHODS: PubMed, EMBASE, the Cochrane Library, and three electronic Chinese databases were searched from December 1, 2019 to April 29, 2020. Eligible studies reporting potential predictors of mortality in patients with COVID-19 were identified. Unadjusted prognostic effect estimates were pooled using the random-effects model if data from at least two studies were available. Adjusted prognostic effect estimates were presented by qualitative analysis. RESULTS: Thirty-six observational studies were identified, of which 27 were included in the meta-analysis. A total of 106 potential risk factors were tested, and the following important predictors were associated with mortality: advanced age, male sex, current smoking status, preexisting comorbidities (especially chronic kidney, respiratory, and cardio-cerebrovascular diseases), symptoms of dyspnea, complications during hospitalization, corticosteroid therapy and a severe condition. Additionally, a series of abnormal laboratory biomarkers of hematologic parameters, hepatorenal function, inflammation, coagulation, and cardiovascular injury were also associated with fatal outcome. CONCLUSION: We identified predictors of mortality in patients with COVID-19. These findings could help healthcare providers take appropriate measures and improve clinical outcomes in such patients. |Adrenal Cortex Hormones/administration & dosage[MESH] |Age Distribution[MESH] |COVID-19/*diagnosis/*mortality[MESH] |Cardiovascular Diseases/epidemiology[MESH] |Comorbidity[MESH] |Databases, Factual[MESH] |Dyspnea/epidemiology[MESH] |Female[MESH] |Hospitalization/statistics & numerical data[MESH] |Humans[MESH] |Inflammation/epidemiology[MESH] |Kidney/physiopathology[MESH] |Liver/physiopathology[MESH] |Male[MESH] |Observational Studies as Topic[MESH] |Prognosis[MESH] |Risk Factors[MESH] |Sex Distribution[MESH]Predictors of mortality in patients with coronavirus disease 2019: a s(epmc).pdf DeepDyve Pubget Overpricing