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10.1002/cnr2.1388 http://scihub22266oqcxt.onion/10.1002/cnr2.1388 34014037!8209944!34014037     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Cancer+Rep+(Hoboken) 2021 ; 4 (5): e1388 Nephropedia Template TP {{tp|p=34014037|t=2021. Rapid real-world data analysis of patients with cancer, with and without COVID-19, across distinct health systems |pdf=|usr=}}{{34014037}} gab.com Text Rapid real-world data analysis of patients with cancer, with and without COVID-19, across distinct health systems JO: Cancer Rep (Hoboken) http://www.kidney.de/pget.php?&tw=1&pmid=34014037 #FOAvip BACKGROUND: The understanding of the impact of COVID-19 in patients with cancer is evolving, with need for rapid analysis. AIMS: This study aims to compare the clinical and demographic characteristics of patients with cancer (with and without COVID-19) and characterize the clinical outcomes of patients with COVID-19 and cancer. METHODS AND RESULTS: Real-world data (RWD) from two health systems were used to identify 146 702 adults diagnosed with cancer between 2015 and 2020; 1267 COVID-19 cases were identified between February 1 and July 30, 2020. Demographic, clinical, and socioeconomic characteristics were extracted. Incidence of all-cause mortality, hospitalizations, and invasive respiratory support was assessed between February 1 and August 14, 2020. Among patients with cancer, patients with COVID-19 were more likely to be Non-Hispanic black (NHB), have active cancer, have comorbidities, and/or live in zip codes with median household income <$30 000. Patients with COVID-19 living in lower-income areas and NHB patients were at greatest risk for hospitalization from pneumonia, fluid and electrolyte disorders, cough, respiratory failure, and acute renal failure and were more likely to receive hydroxychloroquine. All-cause mortality, hospital admission, and invasive respiratory support were more frequent among patients with cancer and COVID-19. Male sex, increasing age, living in zip codes with median household income <$30 000, history of pulmonary circulation disorders, and recent treatment with immune checkpoint inhibitors or chemotherapy were associated with greater odds of all-cause mortality in multivariable logistic regression models. CONCLUSION: RWD can be rapidly leveraged to understand urgent healthcare challenges. Patients with cancer are more vulnerable to COVID-19 effects, especially in the setting of active cancer and comorbidities, with additional risk observed in NHB patients and those living in zip codes with median household income <$30 000. Twit Text FOAVip Rapid real-world data analysis of patients with cancer, with and without COVID-19, across distinct health systems ????Cancer Rep (Hoboken) http://www.kidney.de/pget.php?&tw=1&pmid=34014037 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34014037 #FOAvip English Wikipedia <ref>{{Cite pmid|34014037}}</ref> Rapid real-world data analysis of patients with cancer, with and without COVID-19, across distinct health systems #MMPMID34014037 Hwang C; Izano MA; Thompson MA; Gadgeel SM; Weese JL; Mikkelsen T; Schrag A; Teka M; Walters S; Wolf FM; Hirsch J; Rivera DR; Kluetz PG; Singh H; Brown TD Cancer Rep (Hoboken) 2021[Oct]; 4 (5): e1388 PMID34014037show ga BACKGROUND: The understanding of the impact of COVID-19 in patients with cancer is evolving, with need for rapid analysis. AIMS: This study aims to compare the clinical and demographic characteristics of patients with cancer (with and without COVID-19) and characterize the clinical outcomes of patients with COVID-19 and cancer. METHODS AND RESULTS: Real-world data (RWD) from two health systems were used to identify 146 702 adults diagnosed with cancer between 2015 and 2020; 1267 COVID-19 cases were identified between February 1 and July 30, 2020. Demographic, clinical, and socioeconomic characteristics were extracted. Incidence of all-cause mortality, hospitalizations, and invasive respiratory support was assessed between February 1 and August 14, 2020. Among patients with cancer, patients with COVID-19 were more likely to be Non-Hispanic black (NHB), have active cancer, have comorbidities, and/or live in zip codes with median household income <$30 000. Patients with COVID-19 living in lower-income areas and NHB patients were at greatest risk for hospitalization from pneumonia, fluid and electrolyte disorders, cough, respiratory failure, and acute renal failure and were more likely to receive hydroxychloroquine. All-cause mortality, hospital admission, and invasive respiratory support were more frequent among patients with cancer and COVID-19. Male sex, increasing age, living in zip codes with median household income <$30 000, history of pulmonary circulation disorders, and recent treatment with immune checkpoint inhibitors or chemotherapy were associated with greater odds of all-cause mortality in multivariable logistic regression models. CONCLUSION: RWD can be rapidly leveraged to understand urgent healthcare challenges. Patients with cancer are more vulnerable to COVID-19 effects, especially in the setting of active cancer and comorbidities, with additional risk observed in NHB patients and those living in zip codes with median household income <$30 000. |*Socioeconomic Factors[MESH] |Aged[MESH] |COVID-19/diagnosis/*epidemiology/therapy/virology[MESH] |Comorbidity[MESH] |Data Analysis[MESH] |Female[MESH] |Hospital Mortality[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Neoplasms/complications/*epidemiology/immunology[MESH] |Patient Admission/statistics & numerical data[MESH] |Respiration, Artificial[MESH] |Retrospective Studies[MESH] |Risk Factors[MESH] |SARS-CoV-2/immunology[MESH] |Severity of Illness Index[MESH] |Social Determinants of Health/*statistics & numerical data[MESH]Rapid real-world data analysis of patients with cancer, with and witho(epmc).pdf DeepDyve Pubget Overpricing