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10.19191/EP21.1-2.P100.044 http://scihub22266oqcxt.onion/10.19191/EP21.1-2.P100.044 33884848!ä!33884848 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Epidemiol+Prev 2021 ; 45 (1-2): 100-109 Nephropedia Template TP {{tp|p=33884848|t=2021. Sviluppo di un modello predittivo del rischio di decesso sulla base delle comorbidita in una coorte di 18 286 casi confermati di COVID-19 con almeno 40 anni d'eta |pdf=|usr=}}{{33884848}} gab.com Text Sviluppo di un modello predittivo del rischio di decesso sulla base delle comorbidita in una coorte di 18 286 casi confermati di COVID-19 con almeno 40 anni d'eta JO: Epidemiol Prev http://www.kidney.de/pget.php?&tw=1&pmid=33884848 #FOAvip OBJECTIVES: to develop a risk prediction model for 30-day mortality from COVID?19 in an Italian cohort aged 40 years or older. DESIGN: a population-based retrospective cohort study on prospectively collected data was conducted. SETTING AND PARTICIPANTS: the cohort included all swab positive cases aged 40 years older (No. 18,286) among residents in the territory of the Milan's Agency for Health Protection (ATS-MI) up to 27.04.2020. Data on comorbidities were obtained from the ATS administrative database of chronic conditions. MAIN OUTCOME MEASURES: to predict 30-day mortality risk, a multivariable logistic regression model, including age, gender, and the selected conditions, was developed following the TRIPOD guidelines. Discrimination and calibration of the model were assessed. RESULTS: after age and gender, the most important predictors of 30-day mortality were diabetes, tumour in first-line treatment, chronic heart failure, and complicated diabetes. The bootstrap-validated c-index was 0.78, which suggests that this model is useful in predicting death after COVID-19 infection in swab positive cases. The model had good discrimination (Brier score 0.13) and was well calibrated (Index of prediction accuracy of 14.8%). CONCLUSIONS: a risk prediction model for 30-day mortality in a large COVID-19 cohort aged 40 years or older was developed. In a new epidemic wave, it would help to define groups at different risk and to identify high-risk subjects to target for specific prevention and therapeutic strategies. Twit Text FOAVip Sviluppo di un modello predittivo del rischio di decesso sulla base delle comorbidita in una coorte di 18 286 casi confermati di COVID-19 con almeno 40 anni d'eta ????Epidemiol Prev http://www.kidney.de/pget.php?&tw=1&pmid=33884848 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33884848 #FOAvip English Wikipedia <ref>{{Cite pmid|33884848}}</ref> Sviluppo di un modello predittivo del rischio di decesso sulla base delle comorbidita in una coorte di 18 286 casi confermati di COVID-19 con almeno 40 anni d'eta #MMPMID33884848 Andreano A; Murtas R; Tunesi S; Gervasi F; Magnoni P; Russo AG Epidemiol Prev 2021[Jan]; 45 (1-2): 100-109 PMID33884848show ga OBJECTIVES: to develop a risk prediction model for 30-day mortality from COVID?19 in an Italian cohort aged 40 years or older. DESIGN: a population-based retrospective cohort study on prospectively collected data was conducted. SETTING AND PARTICIPANTS: the cohort included all swab positive cases aged 40 years older (No. 18,286) among residents in the territory of the Milan's Agency for Health Protection (ATS-MI) up to 27.04.2020. Data on comorbidities were obtained from the ATS administrative database of chronic conditions. MAIN OUTCOME MEASURES: to predict 30-day mortality risk, a multivariable logistic regression model, including age, gender, and the selected conditions, was developed following the TRIPOD guidelines. Discrimination and calibration of the model were assessed. RESULTS: after age and gender, the most important predictors of 30-day mortality were diabetes, tumour in first-line treatment, chronic heart failure, and complicated diabetes. The bootstrap-validated c-index was 0.78, which suggests that this model is useful in predicting death after COVID-19 infection in swab positive cases. The model had good discrimination (Brier score 0.13) and was well calibrated (Index of prediction accuracy of 14.8%). CONCLUSIONS: a risk prediction model for 30-day mortality in a large COVID-19 cohort aged 40 years or older was developed. In a new epidemic wave, it would help to define groups at different risk and to identify high-risk subjects to target for specific prevention and therapeutic strategies. |*COVID-19/epidemiology/mortality[MESH] |*Models, Statistical[MESH] |*Risk Assessment/methods[MESH] |Adult[MESH] |Cohort Studies[MESH] |Comorbidity[MESH] |Humans[MESH] |Italy/epidemiology[MESH]Sviluppo di un modello predittivo del rischio di decesso sulla base de(epmc).pdf DeepDyve Pubget Overpricing