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10.1016/j.ejmp.2021.04.022 http://scihub22266oqcxt.onion/10.1016/j.ejmp.2021.04.022 33971530!8084622!33971530     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 269.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Phys+Med 2021 ; 85 (ä): 63-71 Nephropedia Template TP {{tp|p=33971530|t=2021. Robust prediction of mortality of COVID-19 patients based on quantitative, operator-independent, lung CT densitometry |pdf=|usr=}}{{33971530}} gab.com Text Robust prediction of mortality of COVID-19 patients based on quantitative, operator-independent, lung CT densitometry JO: Phys Med http://www.kidney.de/pget.php?&tw=1&pmid=33971530 #FOAvip PURPOSE: To train and validate a predictive model of mortality for hospitalized COVID-19 patients based on lung densitometry. METHODS: Two-hundred-fifty-one patients with respiratory symptoms underwent CT few days after hospitalization. "Aerated" (AV), "consolidated" (CV) and "intermediate" (IV) lung sub-volumes were quantified by an operator-independent method based on individual HU maximum gradient recognition. AV, CV, IV, CV/AV, IV/AV, and HU of the first peak position were extracted. Relevant clinical parameters were prospectively collected. The population was composed by training (n = 166) and validation (n = 85) consecutive cohorts, and backward multi-variate logistic regression was applied on the training group to build a CT_model. Similarly, models including only clinical parameters (CLIN_model) and both CT/clinical parameters (COMB_model) were developed. Model's performances were assessed by goodness-of-fit (H&L-test), calibration and discrimination. Model's performances were tested in the validation group. RESULTS: Forty-three patients died (25/18 in training/validation). CT_model included AVmax (i.e. maximum AV between lungs), CV and CV/AE, while CLIN_model included random glycemia, C-reactive protein and biological drugs (protective). Goodness-of-fit and discrimination were similar (H&L:0.70 vs 0.80; AUC:0.80 vs 0.80). COMB_model including AVmax, CV, CV/AE, random glycemia, biological drugs and active cancer, outperformed both models (H&L:0.91; AUC:0.89, 95%CI:0.82-0.93). All models showed good calibration (R(2):0.77-0.97). Despite several patient's characteristics were different between training and validation cohorts, performances in the validation cohort confirmed good calibration (R(2):0-70-0.81) and discrimination for CT_model/COMB_model (AUC:0.72/0.76), while CLIN_model performed worse (AUC:0.64). CONCLUSIONS: Few automatically extracted densitometry parameters with clear functional meaning predicted mortality of COVID-19 patients. Combined with clinical features, the resulting predictive model showed higher discrimination/calibration. Twit Text FOAVip Robust prediction of mortality of COVID-19 patients based on quantitative, operator-independent, lung CT densitometry ????Phys Med http://www.kidney.de/pget.php?&tw=1&pmid=33971530 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33971530 #FOAvip English Wikipedia <ref>{{Cite pmid|33971530}}</ref> Robust prediction of mortality of COVID-19 patients based on quantitative, operator-independent, lung CT densitometry #MMPMID33971530 Mori M; Palumbo D; De Lorenzo R; Broggi S; Compagnone N; Guazzarotti G; Giorgio Esposito P; Mazzilli A; Steidler S; Pietro Vitali G; Del Vecchio A; Rovere Querini P; De Cobelli F; Fiorino C Phys Med 2021[May]; 85 (ä): 63-71 PMID33971530show ga PURPOSE: To train and validate a predictive model of mortality for hospitalized COVID-19 patients based on lung densitometry. METHODS: Two-hundred-fifty-one patients with respiratory symptoms underwent CT few days after hospitalization. "Aerated" (AV), "consolidated" (CV) and "intermediate" (IV) lung sub-volumes were quantified by an operator-independent method based on individual HU maximum gradient recognition. AV, CV, IV, CV/AV, IV/AV, and HU of the first peak position were extracted. Relevant clinical parameters were prospectively collected. The population was composed by training (n = 166) and validation (n = 85) consecutive cohorts, and backward multi-variate logistic regression was applied on the training group to build a CT_model. Similarly, models including only clinical parameters (CLIN_model) and both CT/clinical parameters (COMB_model) were developed. Model's performances were assessed by goodness-of-fit (H&L-test), calibration and discrimination. Model's performances were tested in the validation group. RESULTS: Forty-three patients died (25/18 in training/validation). CT_model included AVmax (i.e. maximum AV between lungs), CV and CV/AE, while CLIN_model included random glycemia, C-reactive protein and biological drugs (protective). Goodness-of-fit and discrimination were similar (H&L:0.70 vs 0.80; AUC:0.80 vs 0.80). COMB_model including AVmax, CV, CV/AE, random glycemia, biological drugs and active cancer, outperformed both models (H&L:0.91; AUC:0.89, 95%CI:0.82-0.93). All models showed good calibration (R(2):0.77-0.97). Despite several patient's characteristics were different between training and validation cohorts, performances in the validation cohort confirmed good calibration (R(2):0-70-0.81) and discrimination for CT_model/COMB_model (AUC:0.72/0.76), while CLIN_model performed worse (AUC:0.64). CONCLUSIONS: Few automatically extracted densitometry parameters with clear functional meaning predicted mortality of COVID-19 patients. Combined with clinical features, the resulting predictive model showed higher discrimination/calibration. |*COVID-19[MESH] |Densitometry[MESH] |Humans[MESH] |Lung[MESH] |Retrospective Studies[MESH] |SARS-CoV-2[MESH]Robust prediction of mortality of COVID-19 patients based on quantitat(epmc).pdf DeepDyve Pubget Overpricing