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10.1080/07853890.2021.1891453 http://scihub22266oqcxt.onion/10.1080/07853890.2021.1891453 33629918!7919920!33629918     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 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 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Ann+Med 2021 ; 53 (1): 402-409 Nephropedia Template TP {{tp|p=33629918|t=2021. Performance of prediction models for short-term outcome in COVID-19 patients in the emergency department: a retrospective study |pdf=|usr=}}{{33629918}} gab.com Text Performance of prediction models for short-term outcome in COVID-19 patients in the emergency department: a retrospective study JO: Ann Med http://www.kidney.de/pget.php?&tw=1&pmid=33629918 #FOAvip INTRODUCTION: Coronavirus disease 2019 (COVID-19) has a high burden on the healthcare system. Prediction models may assist in triaging patients. We aimed to assess the value of several prediction models in COVID-19 patients in the emergency department (ED). METHODS: In this retrospective study, ED patients with COVID-19 were included. Prediction models were selected based on their feasibility. Primary outcome was 30-day mortality, secondary outcomes were 14-day mortality and a composite outcome of 30-day mortality and admission to medium care unit (MCU) or intensive care unit (ICU). The discriminatory performance of the prediction models was assessed using an area under the receiver operating characteristic curve (AUC). RESULTS: We included 403 patients. Thirty-day mortality was 23.6%, 14-day mortality was 19.1%, 66 patients (16.4%) were admitted to ICU, 48 patients (11.9%) to MCU, and 152 patients (37.7%) met the composite endpoint. Eleven prediction models were included. The RISE UP score and 4 C mortality scores showed very good discriminatory performance for 30-day mortality (AUC 0.83 and 0.84, 95% CI 0.79-0.88 for both), significantly higher than that of the other models. CONCLUSION: The RISE UP score and 4 C mortality score can be used to recognise patients at high risk for poor outcome and may assist in guiding decision-making and allocating resources. Twit Text FOAVip Performance of prediction models for short-term outcome in COVID-19 patients in the emergency department: a retrospective study ????Ann Med http://www.kidney.de/pget.php?&tw=1&pmid=33629918 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33629918 #FOAvip English Wikipedia <ref>{{Cite pmid|33629918}}</ref> Performance of prediction models for short-term outcome in COVID-19 patients in the emergency department: a retrospective study #MMPMID33629918 van Dam PMEL; Zelis N; van Kuijk SMJ; Linkens AEMJH; Bruggemann RAG; Spaetgens B; van der Horst ICC; Stassen PM Ann Med 2021[Dec]; 53 (1): 402-409 PMID33629918show ga INTRODUCTION: Coronavirus disease 2019 (COVID-19) has a high burden on the healthcare system. Prediction models may assist in triaging patients. We aimed to assess the value of several prediction models in COVID-19 patients in the emergency department (ED). METHODS: In this retrospective study, ED patients with COVID-19 were included. Prediction models were selected based on their feasibility. Primary outcome was 30-day mortality, secondary outcomes were 14-day mortality and a composite outcome of 30-day mortality and admission to medium care unit (MCU) or intensive care unit (ICU). The discriminatory performance of the prediction models was assessed using an area under the receiver operating characteristic curve (AUC). RESULTS: We included 403 patients. Thirty-day mortality was 23.6%, 14-day mortality was 19.1%, 66 patients (16.4%) were admitted to ICU, 48 patients (11.9%) to MCU, and 152 patients (37.7%) met the composite endpoint. Eleven prediction models were included. The RISE UP score and 4 C mortality scores showed very good discriminatory performance for 30-day mortality (AUC 0.83 and 0.84, 95% CI 0.79-0.88 for both), significantly higher than that of the other models. CONCLUSION: The RISE UP score and 4 C mortality score can be used to recognise patients at high risk for poor outcome and may assist in guiding decision-making and allocating resources. |Aged[MESH] |COVID-19/diagnosis/*mortality[MESH] |Emergency Service, Hospital/*statistics & numerical data[MESH] |Feasibility Studies[MESH] |Female[MESH] |Hospital Mortality[MESH] |Humans[MESH] |Length of Stay/statistics & numerical data[MESH] |Logistic Models[MESH] |Male[MESH] |Middle Aged[MESH] |Netherlands/epidemiology[MESH] |Prognosis[MESH] |ROC Curve[MESH] |Retrospective Studies[MESH] |Risk Assessment/methods[MESH]Performance of prediction models for short-term outcome in COVID-19 pa(epmc).pdf DeepDyve Pubget Overpricing