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10.20452/pamw.15422 http://scihub22266oqcxt.onion/10.20452/pamw.15422 32500700!ä!32500700 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 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Pol+Arch+Intern+Med 2020 ; 130 (7-8): 629-634 Nephropedia Template TP {{tp|p=32500700|t=2020. Novel coronavirus disease 2019: predicting prognosis with a computed tomography-based disease severity score and clinical laboratory data |pdf=|usr=}}{{32500700}} gab.com Text Novel coronavirus disease 2019: predicting prognosis with a computed tomography-based disease severity score and clinical laboratory data JO: Pol Arch Intern Med http://www.kidney.de/pget.php?&tw=1&pmid=32500700 #FOAvip INTRODUCTION: Currently, there are known contributing factors but no comprehensive methods for predicting the mortality risk or intensive care unit (ICU) admission in patients with novel coronavirus disease 2019 (COVID?19). OBJECTIVES: The aim of this study was to explore risk factors for mortality and ICU admission in patients with COVID?19, using computed tomography (CT) combined with clinical laboratory data. PATIENTS AND METHODS: Patients with polymerase chain reaction-confirmed COVID?19 (n = 63) from university hospitals in Tehran, Iran, were included. All patients underwent CT examination. Subsequently, a total CT score and the number of involved lung lobes were calculated and compared against collected laboratory and clinical characteristics. Univariable and multivariable proportional hazard analyses were used to determine the association among CT, laboratory and clinical data, ICU admission, and in?hospital death. RESULTS: By univariable analysis, in?hospital mortality was higher in patients with lower oxygen saturation on admission (below 88%), higher CT scores, and a higher number of lung lobes (more than 4) involved with a diffuse parenchymal pattern. By multivariable analysis, in?hospital mortality was higher in those with oxygen saturation below 88% on admission and a higher number of lung lobes involved with a diffuse parenchymal pattern. The risk of ICU admission was higher in patients with comorbidities (hypertension and ischemic heart disease), arterial oxygen saturation below 88%, and pericardial effusion. CONCLUSIONS: We can identify factors affecting in?hospital death and ICU admission in COVID-19. This can help clinicians to determine which patients are likely to require ICU admission and to inform strategic healthcare planning in critical conditions such as the COVID?19 pandemic. Twit Text FOAVip Novel coronavirus disease 2019: predicting prognosis with a computed tomography-based disease severity score and clinical laboratory data ????Pol Arch Intern Med http://www.kidney.de/pget.php?&tw=1&pmid=32500700 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32500700 #FOAvip English Wikipedia <ref>{{Cite pmid|32500700}}</ref> Novel coronavirus disease 2019: predicting prognosis with a computed tomography-based disease severity score and clinical laboratory data #MMPMID32500700 Sabri A; Davarpanah AH; Mahdavi A; Abrishami A; Khazaei M; Heydari S; Asgari R; Nekooghadam SM; Dobranowski J; Taheri MS Pol Arch Intern Med 2020[Aug]; 130 (7-8): 629-634 PMID32500700show ga INTRODUCTION: Currently, there are known contributing factors but no comprehensive methods for predicting the mortality risk or intensive care unit (ICU) admission in patients with novel coronavirus disease 2019 (COVID?19). OBJECTIVES: The aim of this study was to explore risk factors for mortality and ICU admission in patients with COVID?19, using computed tomography (CT) combined with clinical laboratory data. PATIENTS AND METHODS: Patients with polymerase chain reaction-confirmed COVID?19 (n = 63) from university hospitals in Tehran, Iran, were included. All patients underwent CT examination. Subsequently, a total CT score and the number of involved lung lobes were calculated and compared against collected laboratory and clinical characteristics. Univariable and multivariable proportional hazard analyses were used to determine the association among CT, laboratory and clinical data, ICU admission, and in?hospital death. RESULTS: By univariable analysis, in?hospital mortality was higher in patients with lower oxygen saturation on admission (below 88%), higher CT scores, and a higher number of lung lobes (more than 4) involved with a diffuse parenchymal pattern. By multivariable analysis, in?hospital mortality was higher in those with oxygen saturation below 88% on admission and a higher number of lung lobes involved with a diffuse parenchymal pattern. The risk of ICU admission was higher in patients with comorbidities (hypertension and ischemic heart disease), arterial oxygen saturation below 88%, and pericardial effusion. CONCLUSIONS: We can identify factors affecting in?hospital death and ICU admission in COVID-19. This can help clinicians to determine which patients are likely to require ICU admission and to inform strategic healthcare planning in critical conditions such as the COVID?19 pandemic. |*Real-Time Polymerase Chain Reaction[MESH] |Adult[MESH] |Age Distribution[MESH] |Aged[MESH] |Betacoronavirus/*isolation & purification[MESH] |COVID-19[MESH] |COVID-19 Testing[MESH] |Clinical Laboratory Techniques[MESH] |Coronavirus Infections/diagnosis/*diagnostic imaging/epidemiology[MESH] |Female[MESH] |Humans[MESH] |Iran[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnostic imaging/epidemiology[MESH] |Poland/epidemiology[MESH] |SARS-CoV-2[MESH] |Sex Distribution[MESH] |Tomography, X-Ray Computed[MESH]Novel coronavirus disease 2019: predicting prognosis with a computed (epmc).pdf DeepDyve Pubget Overpricing