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10.1016/j.cca.2020.11.019 http://scihub22266oqcxt.onion/10.1016/j.cca.2020.11.019 33279501!7836550!33279501     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 278.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 278.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 278.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Clin+Chim+Acta 2021 ; 512 (ä): 49-57 Nephropedia Template TP {{tp|p=33279501|t=2021. A nomogram to early predict isolation length for non-severe COVID-19 patients based on laboratory investigation: A multicenter retrospective study in Zhejiang Province, China |pdf=|usr=}}{{33279501}} gab.com Text A nomogram to early predict isolation length for non-severe COVID-19 patients based on laboratory investigation: A multicenter retrospective study in Zhejiang Province, China JO: Clin Chim Acta http://www.kidney.de/pget.php?&tw=1&pmid=33279501 #FOAvip BACKGROUND: Majority coronavirus disease 2019 (COVID-19) patients are classified as mild and moderate (non-severe) diseases. We aim to develop a model to predict isolation length for non-severe patients. METHODS: Among 188 non-severe patients, 96 patients were enrolled as training cohort to identify factors associated with isolation length via Cox regression model and develop a nomogram. Other 92 patients formed as validation cohort to validate nomogram. Concordance index (C-index), area under the curve (AUC) and calibration curves were used to evaluated nomogram. RESULTS: Increasing absolute eosinophil count (AEC) after admission was correlated with shorter isolation length (P = 0.02). Baseline activated partial thromboplastin time (APTT) > 30 s was correlated with longer isolation length (P = 0.03). A nomogram to predict isolation probability at 11-, 16- and 21-day was developed and validated. The C-indices of training and validation cohort were 0.604 and 0.682 respectively. Both cohorts showed a good discriminative ability (AUC, 11-day: 0.646 vs 0.730; 16-day: 0.663 vs 0.750; 21-day: 0.711 vs 0.783; respectively) and calibration power. CONCLUSIONS: Baseline APTT and dynamic change of AEC were two significant factors associated with isolation length of non-severe patients. Nomogram could predict isolation probability for each patient to estimate appropriate quarantine length. Twit Text FOAVip A nomogram to early predict isolation length for non-severe COVID-19 patients based on laboratory investigation: A multicenter retrospective study in Zhejiang Province, China ????Clin Chim Acta http://www.kidney.de/pget.php?&tw=1&pmid=33279501 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33279501 #FOAvip English Wikipedia <ref>{{Cite pmid|33279501}}</ref> A nomogram to early predict isolation length for non-severe COVID-19 patients based on laboratory investigation: A multicenter retrospective study in Zhejiang Province, China #MMPMID33279501 Xia Y; Zhang Y; Yuan S; Chen J; Zheng W; Xu X; Xie X; Zhang J Clin Chim Acta 2021[Jan]; 512 (ä): 49-57 PMID33279501show ga BACKGROUND: Majority coronavirus disease 2019 (COVID-19) patients are classified as mild and moderate (non-severe) diseases. We aim to develop a model to predict isolation length for non-severe patients. METHODS: Among 188 non-severe patients, 96 patients were enrolled as training cohort to identify factors associated with isolation length via Cox regression model and develop a nomogram. Other 92 patients formed as validation cohort to validate nomogram. Concordance index (C-index), area under the curve (AUC) and calibration curves were used to evaluated nomogram. RESULTS: Increasing absolute eosinophil count (AEC) after admission was correlated with shorter isolation length (P = 0.02). Baseline activated partial thromboplastin time (APTT) > 30 s was correlated with longer isolation length (P = 0.03). A nomogram to predict isolation probability at 11-, 16- and 21-day was developed and validated. The C-indices of training and validation cohort were 0.604 and 0.682 respectively. Both cohorts showed a good discriminative ability (AUC, 11-day: 0.646 vs 0.730; 16-day: 0.663 vs 0.750; 21-day: 0.711 vs 0.783; respectively) and calibration power. CONCLUSIONS: Baseline APTT and dynamic change of AEC were two significant factors associated with isolation length of non-severe patients. Nomogram could predict isolation probability for each patient to estimate appropriate quarantine length. |*Nomograms[MESH] |*Physical Distancing[MESH] |*Quarantine[MESH] |Adult[MESH] |Antiviral Agents/therapeutic use[MESH] |Area Under Curve[MESH] |COVID-19 Testing[MESH] |COVID-19/blood/*etiology/*therapy[MESH] |China[MESH] |Eosinophils[MESH] |Female[MESH] |Hospitalization[MESH] |Humans[MESH] |Leukocyte Count[MESH] |Male[MESH] |Middle Aged[MESH] |Partial Thromboplastin Time[MESH] |Proportional Hazards Models[MESH] |Reproducibility of Results[MESH] |Retrospective Studies[MESH]A nomogram to early predict isolation length for non-severe COVID-19 p(epmc).pdf DeepDyve Pubget Overpricing