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10.1371/journal.pone.0236618 http://scihub22266oqcxt.onion/10.1371/journal.pone.0236618 32730358!7392248!32730358     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       PLoS+One 2020 ; 15 (7): e0236618 Nephropedia Template TP {{tp|p=32730358|t=2020. Prediction model and risk scores of ICU admission and mortality in COVID-19 |pdf=|usr=}}{{32730358}} gab.com Text Prediction model and risk scores of ICU admission and mortality in COVID-19 JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32730358 #FOAvip This study aimed to develop risk scores based on clinical characteristics at presentation to predict intensive care unit (ICU) admission and mortality in COVID-19 patients. 641 hospitalized patients with laboratory-confirmed COVID-19 were selected from 4997 persons under investigation. We performed a retrospective review of medical records of demographics, comorbidities and laboratory tests at the initial presentation. Primary outcomes were ICU admission and death. Logistic regression was used to identify independent clinical variables predicting the two outcomes. The model was validated by splitting the data into 70% for training and 30% for testing. Performance accuracy was evaluated using area under the curve (AUC) of the receiver operating characteristic analysis (ROC). Five significant variables predicting ICU admission were lactate dehydrogenase, procalcitonin, pulse oxygen saturation, smoking history, and lymphocyte count. Seven significant variables predicting mortality were heart failure, procalcitonin, lactate dehydrogenase, chronic obstructive pulmonary disease, pulse oxygen saturation, heart rate, and age. The mortality group uniquely contained cardiopulmonary variables. The risk score model yielded good accuracy with an AUC of 0.74 ([95% CI, 0.63-0.85], p = 0.001) for predicting ICU admission and 0.83 ([95% CI, 0.73-0.92], p<0.001) for predicting mortality for the testing dataset. This study identified key independent clinical variables that predicted ICU admission and mortality associated with COVID-19. This risk score system may prove useful for frontline physicians in clinical decision-making under time-sensitive and resource-constrained environment. Twit Text FOAVip Prediction model and risk scores of ICU admission and mortality in COVID-19 ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32730358 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32730358 #FOAvip English Wikipedia <ref>{{Cite pmid|32730358}}</ref> Prediction model and risk scores of ICU admission and mortality in COVID-19 #MMPMID32730358 Zhao Z; Chen A; Hou W; Graham JM; Li H; Richman PS; Thode HC; Singer AJ; Duong TQ PLoS One 2020[]; 15 (7): e0236618 PMID32730358show ga This study aimed to develop risk scores based on clinical characteristics at presentation to predict intensive care unit (ICU) admission and mortality in COVID-19 patients. 641 hospitalized patients with laboratory-confirmed COVID-19 were selected from 4997 persons under investigation. We performed a retrospective review of medical records of demographics, comorbidities and laboratory tests at the initial presentation. Primary outcomes were ICU admission and death. Logistic regression was used to identify independent clinical variables predicting the two outcomes. The model was validated by splitting the data into 70% for training and 30% for testing. Performance accuracy was evaluated using area under the curve (AUC) of the receiver operating characteristic analysis (ROC). Five significant variables predicting ICU admission were lactate dehydrogenase, procalcitonin, pulse oxygen saturation, smoking history, and lymphocyte count. Seven significant variables predicting mortality were heart failure, procalcitonin, lactate dehydrogenase, chronic obstructive pulmonary disease, pulse oxygen saturation, heart rate, and age. The mortality group uniquely contained cardiopulmonary variables. The risk score model yielded good accuracy with an AUC of 0.74 ([95% CI, 0.63-0.85], p = 0.001) for predicting ICU admission and 0.83 ([95% CI, 0.73-0.92], p<0.001) for predicting mortality for the testing dataset. This study identified key independent clinical variables that predicted ICU admission and mortality associated with COVID-19. This risk score system may prove useful for frontline physicians in clinical decision-making under time-sensitive and resource-constrained environment. |*Betacoronavirus[MESH] |*Intensive Care Units[MESH] |*Models, Theoretical[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |Area Under Curve[MESH] |COVID-19[MESH] |Clinical Decision-Making[MESH] |Coronavirus Infections/*epidemiology/*mortality/virology[MESH] |Female[MESH] |Hospitals, University[MESH] |Humans[MESH] |Logistic Models[MESH] |Male[MESH] |Middle Aged[MESH] |New York/epidemiology[MESH] |Pandemics[MESH] |Patient Admission/*trends[MESH] |Pneumonia, Viral/*epidemiology/*mortality/virology[MESH] |Prognosis[MESH] |ROC Curve[MESH] |Retrospective Studies[MESH] |Risk Factors[MESH]Prediction model and risk scores of ICU admission and mortality in COV(epmc).pdf DeepDyve Pubget Overpricing