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10.1016/j.medj.2020.12.013 http://scihub22266oqcxt.onion/10.1016/j.medj.2020.12.013 33521746!7831644!33521746     free     free     free Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 269.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 269.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Med 2021 ; 2 (4): 435-447.e4 Nephropedia Template TP {{tp|p=33521746|t=2021. Development and validation of a risk score using complete blood count to predict in-hospital mortality in COVID-19 patients |pdf=|usr=}}{{33521746}} gab.com Text Development and validation of a risk score using complete blood count to predict in-hospital mortality in COVID-19 patients JO: Med http://www.kidney.de/pget.php?&tw=1&pmid=33521746 #FOAvip BACKGROUND: To develop a sensitive risk score predicting the risk of mortality in patients with coronavirus disease 2019 (COVID-19) using complete blood count (CBC). METHODS: We performed a retrospective cohort study from a total of 13,138 inpatients with COVID-19 in Hubei, China, and Milan, Italy. Among them, 9,810 patients with >/=2 CBC records from Hubei were assigned to the training cohort. CBC parameters were analyzed as potential predictors for all-cause mortality and were selected by the generalized linear mixed model (GLMM). FINDINGS: Five risk factors were derived to construct a composite score (PAWNN score) using the Cox regression model, including platelet counts, age, white blood cell counts, neutrophil counts, and neutrophil:lymphocyte ratio. The PAWNN score showed good accuracy for predicting mortality in 10-fold cross-validation (AUROCs 0.92-0.93) and subsets with different quartile intervals of follow-up and preexisting diseases. The performance of the score was further validated in 2,949 patients with only 1 CBC record from the Hubei cohort (AUROC 0.97) and 227 patients from the Italian cohort (AUROC 0.80). The latent Markov model (LMM) demonstrated that the PAWNN score has good prediction power for transition probabilities between different latent conditions. CONCLUSIONS: The PAWNN score is a simple and accurate risk assessment tool that can predict the mortality for COVID-19 patients during their entire hospitalization. This tool can assist clinicians in prioritizing medical treatment of COVID-19 patients. FUNDING: This work was supported by National Key R&D Program of China (2016YFF0101504, 2016YFF0101505, 2020YFC2004702, 2020YFC0845500), the Key R&D Program of Guangdong Province (2020B1111330003), and the medical flight plan of Wuhan University (TFJH2018006). Twit Text FOAVip Development and validation of a risk score using complete blood count to predict in-hospital mortality in COVID-19 patients ????Med http://www.kidney.de/pget.php?&tw=1&pmid=33521746 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33521746 #FOAvip English Wikipedia <ref>{{Cite pmid|33521746}}</ref> Development and validation of a risk score using complete blood count to predict in-hospital mortality in COVID-19 patients #MMPMID33521746 Liu H; Chen J; Yang Q; Lei F; Zhang C; Qin JJ; Chen Z; Zhu L; Song X; Bai L; Huang X; Liu W; Zhou F; Chen MM; Zhao YC; Zhang XJ; She ZG; Xu Q; Ma X; Zhang P; Ji YX; Zhang X; Yang J; Xie J; Ye P; Azzolini E; Aghemo A; Ciccarelli M; Condorelli G; Stefanini GG; Xia J; Zhang BH; Yuan Y; Wei X; Wang Y; Cai J; Li H Med 2021[Apr]; 2 (4): 435-447.e4 PMID33521746show ga BACKGROUND: To develop a sensitive risk score predicting the risk of mortality in patients with coronavirus disease 2019 (COVID-19) using complete blood count (CBC). METHODS: We performed a retrospective cohort study from a total of 13,138 inpatients with COVID-19 in Hubei, China, and Milan, Italy. Among them, 9,810 patients with >/=2 CBC records from Hubei were assigned to the training cohort. CBC parameters were analyzed as potential predictors for all-cause mortality and were selected by the generalized linear mixed model (GLMM). FINDINGS: Five risk factors were derived to construct a composite score (PAWNN score) using the Cox regression model, including platelet counts, age, white blood cell counts, neutrophil counts, and neutrophil:lymphocyte ratio. The PAWNN score showed good accuracy for predicting mortality in 10-fold cross-validation (AUROCs 0.92-0.93) and subsets with different quartile intervals of follow-up and preexisting diseases. The performance of the score was further validated in 2,949 patients with only 1 CBC record from the Hubei cohort (AUROC 0.97) and 227 patients from the Italian cohort (AUROC 0.80). The latent Markov model (LMM) demonstrated that the PAWNN score has good prediction power for transition probabilities between different latent conditions. CONCLUSIONS: The PAWNN score is a simple and accurate risk assessment tool that can predict the mortality for COVID-19 patients during their entire hospitalization. This tool can assist clinicians in prioritizing medical treatment of COVID-19 patients. FUNDING: This work was supported by National Key R&D Program of China (2016YFF0101504, 2016YFF0101505, 2020YFC2004702, 2020YFC0845500), the Key R&D Program of Guangdong Province (2020B1111330003), and the medical flight plan of Wuhan University (TFJH2018006). |*COVID-19[MESH] |Blood Cell Count[MESH] |Hospital Mortality[MESH] |Humans[MESH] |Prognosis[MESH] |Retrospective Studies[MESH] |Risk Factors[MESH]Development and validation of a risk score using complete blood count (epmc).pdf DeepDyve Pubget Overpricing