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10.26355/eurrev_202010_23249 http://scihub22266oqcxt.onion/10.26355/eurrev_202010_23249 33090436!ä!33090436 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       Eur+Rev+Med+Pharmacol+Sci 2020 ; 24 (19): 10247-10257 Nephropedia Template TP {{tp|p=33090436|t=2020. A new COVID-19 prediction scoring model for in-hospital mortality: experiences from Turkey, single center retrospective cohort analysis |pdf=|usr=}}{{33090436}} gab.com Text A new COVID-19 prediction scoring model for in-hospital mortality: experiences from Turkey, single center retrospective cohort analysis JO: Eur Rev Med Pharmacol Sci http://www.kidney.de/pget.php?&tw=1&pmid=33090436 #FOAvip OBJECTIVE: Although many studies reported prognostic factors proceeding to severity of COVID-19 patients, in none of the article a prediction scoring model has been proposed. In this article a new prediction tool is presented in combination of Turkish experience during pandemic. MATERIALS AND METHODS: Laboratory and clinical data of 397 over 798 confirmed COVID-19 patients from Gulhane Training and Research Hospital electronic medical record system were included into this retrospective cohort study between the dates of 23 March to 18 May 2020. Patient demographics, peripheral venous blood parameters, symptoms at admission, in hospital mortality data were collected. Non-survivor and survivor patients were compared to find out a prediction scoring model for mortality. RESULTS: There was 34 [8.56% (95% CI:0.06-0.11)] mortality during study period. Mean age of patients was 57.1+/-16.7 years. Older age, comorbid diseases, symptoms, such as fever, dyspnea, fatigue and gastrointestinal and WBC, neutrophil, lymphocyte count, C-reactive protein, neutrophil-to-lymphocyte ratio of patients in non-survivors were significantly higher. Univariate analysis demonstrated that OR for prognostic nutritional index (PNI) tertile 1 was 18.57 (95% CI: 4.39-78.65, p<0.05) compared to tertile 2. Performance statistics of prediction scoring method showed 98% positive predictive value for criteria 1. CONCLUSIONS: It is crucial to constitute prognostic clinical and laboratory parameters for faster delineation of patients who are prone to worse prognosis. Suggested prediction scoring method may guide healthcare professional to discriminate severe COVID-19 patients and provide prompt intensive therapies which is highly important due to rapid progression leading to mortality. Twit Text FOAVip A new COVID-19 prediction scoring model for in-hospital mortality: experiences from Turkey, single center retrospective cohort analysis ????Eur Rev Med Pharmacol Sci http://www.kidney.de/pget.php?&tw=1&pmid=33090436 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33090436 #FOAvip English Wikipedia <ref>{{Cite pmid|33090436}}</ref> A new COVID-19 prediction scoring model for in-hospital mortality: experiences from Turkey, single center retrospective cohort analysis #MMPMID33090436 Doganci S; Ince ME; Ors N; Yildirim AK; Sir E; Karabacak K; Eksert S; Ozgurtas T; Tasci C; Dogan D; Ozkan G; Cosar A; Gulcelik MA; Aydin K; Yildirim V; Erdol C Eur Rev Med Pharmacol Sci 2020[Oct]; 24 (19): 10247-10257 PMID33090436show ga OBJECTIVE: Although many studies reported prognostic factors proceeding to severity of COVID-19 patients, in none of the article a prediction scoring model has been proposed. In this article a new prediction tool is presented in combination of Turkish experience during pandemic. MATERIALS AND METHODS: Laboratory and clinical data of 397 over 798 confirmed COVID-19 patients from Gulhane Training and Research Hospital electronic medical record system were included into this retrospective cohort study between the dates of 23 March to 18 May 2020. Patient demographics, peripheral venous blood parameters, symptoms at admission, in hospital mortality data were collected. Non-survivor and survivor patients were compared to find out a prediction scoring model for mortality. RESULTS: There was 34 [8.56% (95% CI:0.06-0.11)] mortality during study period. Mean age of patients was 57.1+/-16.7 years. Older age, comorbid diseases, symptoms, such as fever, dyspnea, fatigue and gastrointestinal and WBC, neutrophil, lymphocyte count, C-reactive protein, neutrophil-to-lymphocyte ratio of patients in non-survivors were significantly higher. Univariate analysis demonstrated that OR for prognostic nutritional index (PNI) tertile 1 was 18.57 (95% CI: 4.39-78.65, p<0.05) compared to tertile 2. Performance statistics of prediction scoring method showed 98% positive predictive value for criteria 1. CONCLUSIONS: It is crucial to constitute prognostic clinical and laboratory parameters for faster delineation of patients who are prone to worse prognosis. Suggested prediction scoring method may guide healthcare professional to discriminate severe COVID-19 patients and provide prompt intensive therapies which is highly important due to rapid progression leading to mortality. |*Hospital Mortality[MESH] |*Models, Statistical[MESH] |Age Factors[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |COVID-19/*diagnosis/*mortality[MESH] |Cohort Studies[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Prognosis[MESH] |Retrospective Studies[MESH] |Risk Factors[MESH] |SARS-CoV-2[MESH] |Survivors/*statistics & numerical data[MESH]A new COVID-19 prediction scoring model for in-hospital mortality: exp(epmc).pdf DeepDyve Pubget Overpricing