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10.1016/j.ijid.2020.09.022 http://scihub22266oqcxt.onion/10.1016/j.ijid.2020.09.022 32947055!7491462!32947055     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Infect+Dis 2020 ; 101 (ä): 74-82 Nephropedia Template TP {{tp|p=32947055|t=2020. Development and validation of risk prediction models for COVID-19 positivity in a hospital setting |pdf=|usr=}}{{32947055}} gab.com Text Development and validation of risk prediction models for COVID-19 positivity in a hospital setting JO: Int J Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=32947055 #FOAvip OBJECTIVES: To develop: (1) two validated risk prediction models for coronavirus disease-2019 (COVID-19) positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation. METHODS: Patients with and without COVID-19 were included from 4 Hong Kong hospitals. The database was randomly split into 2:1: for model development database (n = 895) and validation database (n = 435). Multivariable logistic regression was utilised for model creation and validated with the Hosmer-Lemeshow (H-L) test and calibration plot. Nomograms and probabilities set at 0.1, 0.2, 0.4 and 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). RESULTS: A total of 1330 patients (mean age 58.2 +/- 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. The first prediction model developed had age, total white blood cell count, chest x-ray appearances and contact history as significant predictors (AUC = 0.911 [CI = 0.880-0.941]). The second model developed has the same variables except contact history (AUC = 0.880 [CI = 0.844-0.916]). Both were externally validated on the H-L test (p = 0.781 and 0.155, respectively) and calibration plot. Models were converted to nomograms. Lower probabilities give higher sensitivity and NPV; higher probabilities give higher specificity and PPV. CONCLUSION: Two simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation. Twit Text FOAVip Development and validation of risk prediction models for COVID-19 positivity in a hospital setting ????Int J Infect Dis http://www.kidney.de/pget.php?&tw=1&pmid=32947055 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32947055 #FOAvip English Wikipedia <ref>{{Cite pmid|32947055}}</ref> Development and validation of risk prediction models for COVID-19 positivity in a hospital setting #MMPMID32947055 Ng MY; Wan EYF; Wong HYF; Leung ST; Lee JCY; Chin TW; Lo CSY; Lui MM; Chan EHT; Fong AH; Fung SY; Ching OH; Chiu KW; Chung TWH; Vardhanbhuti V; Lam HYS; To KKW; Chiu JLF; Lam TPW; Khong PL; Liu RWT; Chan JWM; Wu AKL; Lung KC; Hung IFN; Lau CS; Kuo MD; Ip MS Int J Infect Dis 2020[Dec]; 101 (ä): 74-82 PMID32947055show ga OBJECTIVES: To develop: (1) two validated risk prediction models for coronavirus disease-2019 (COVID-19) positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation. METHODS: Patients with and without COVID-19 were included from 4 Hong Kong hospitals. The database was randomly split into 2:1: for model development database (n = 895) and validation database (n = 435). Multivariable logistic regression was utilised for model creation and validated with the Hosmer-Lemeshow (H-L) test and calibration plot. Nomograms and probabilities set at 0.1, 0.2, 0.4 and 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). RESULTS: A total of 1330 patients (mean age 58.2 +/- 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. The first prediction model developed had age, total white blood cell count, chest x-ray appearances and contact history as significant predictors (AUC = 0.911 [CI = 0.880-0.941]). The second model developed has the same variables except contact history (AUC = 0.880 [CI = 0.844-0.916]). Both were externally validated on the H-L test (p = 0.781 and 0.155, respectively) and calibration plot. Models were converted to nomograms. Lower probabilities give higher sensitivity and NPV; higher probabilities give higher specificity and PPV. CONCLUSION: Two simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation. |*SARS-CoV-2[MESH] |Adult[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |Area Under Curve[MESH] |COVID-19/*diagnosis/etiology[MESH] |Female[MESH] |Hospitals[MESH] |Humans[MESH] |Logistic Models[MESH] |Male[MESH] |Middle Aged[MESH] |Nomograms[MESH]Development and validation of risk prediction models for COVID-19 posi(epmc).pdf DeepDyve Pubget Overpricing