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10.1186/s12891-021-04303-8 http://scihub22266oqcxt.onion/10.1186/s12891-021-04303-8 34182966!8236737!34182966     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34182966&cmd=llinks): Failed to open stream: HTTP request failed! 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Survival analysis and influence of the surgical aggression of a cohort of orthopedic and trauma patients in a non-controlled spread COVID-19 scenario |pdf=|usr=}}{{34182966}} gab.com Text Survival analysis and influence of the surgical aggression of a cohort of orthopedic and trauma patients in a non-controlled spread COVID-19 scenario JO: BMC Musculoskelet Disord http://www.kidney.de/pget.php?&tw=1&pmid=34182966 #FOAvip BACKGROUND: Determining the infection rate and mortality probability in healthy patients who have undergone orthopedic and trauma surgeries (OTS) during a period of uncontrolled COVID-19 transmission may help to inform preparations for future waves. This study performed a survival analysis in a cohort of non-infected OTS patients and determined the effect of COVID-19 on mortality. METHODS: This observational study included 184 patients who underwent OTS in the month before surgical activities ceased and before the implementation of special measures. Four groups of surgery (GS) were established based on the location of the surgery and the grade of inflammation produced. Crude risk of infection and infection rates were assessed. Survival and failure functions by GS were analyzed. Comparison of the Kaplan-Meier survival curves by GS was assessed. Cox regression and Fine-Gray models were used to determine the effect of different confounders on mortality. RESULTS: The crude risk of COVID-19 diagnosis was 14.13% (95% CI: 9.83-19.90%). The total incidence rate was 2.67 (1000 person-days, 95% CI: 1.74-3.91). At the end of follow-up, there was a 94.42% chance of surviving 76 days or more after OTS. The differences in K-M survivor curves by GS indicated that GS 4 presented a lower survival function (Mantel-Cox test, p = 0.024; Wilcoxon-Breslow test, p = 0.044; Tarone-Ware test, p = 0.032). One of the best models to determine the association with mortality was the age-adjusted model for GS, high blood pressure, and respiratory history, with a hazard ratio of 1.112 in Cox regression analysis (95% CI: 1.005-1.230) and a sub hazard ratio of 1.111 (95% CI: 1.046-1.177) in Fine-Gray regression analysis for competitive risk. CONCLUSIONS: The infection risk after OTS was similar to that of the general population in a community transmission area; the grade of surgical aggression did not influence this rate. The survival probability was extremely high if patients had not previously been infected. With higher grades of surgical aggression, the risk of mortality was higher in OTS patients. Adjusting for age and other confounders (e.g., GS, high blood pressure and respiratory history) was associated with higher mortality rates. Twit Text FOAVip Survival analysis and influence of the surgical aggression of a cohort of orthopedic and trauma patients in a non-controlled spread COVID-19 scenario ????BMC Musculoskelet Disord http://www.kidney.de/pget.php?&tw=1&pmid=34182966 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34182966 #FOAvip English Wikipedia <ref>{{Cite pmid|34182966}}</ref> Survival analysis and influence of the surgical aggression of a cohort of orthopedic and trauma patients in a non-controlled spread COVID-19 scenario #MMPMID34182966 Corella F; Rosales RS; Guzman Domenech D; Canones Martin M; Larrainzar-Garijo R BMC Musculoskelet Disord 2021[Jun]; 22 (1): 594 PMID34182966show ga BACKGROUND: Determining the infection rate and mortality probability in healthy patients who have undergone orthopedic and trauma surgeries (OTS) during a period of uncontrolled COVID-19 transmission may help to inform preparations for future waves. This study performed a survival analysis in a cohort of non-infected OTS patients and determined the effect of COVID-19 on mortality. METHODS: This observational study included 184 patients who underwent OTS in the month before surgical activities ceased and before the implementation of special measures. Four groups of surgery (GS) were established based on the location of the surgery and the grade of inflammation produced. Crude risk of infection and infection rates were assessed. Survival and failure functions by GS were analyzed. Comparison of the Kaplan-Meier survival curves by GS was assessed. Cox regression and Fine-Gray models were used to determine the effect of different confounders on mortality. RESULTS: The crude risk of COVID-19 diagnosis was 14.13% (95% CI: 9.83-19.90%). The total incidence rate was 2.67 (1000 person-days, 95% CI: 1.74-3.91). At the end of follow-up, there was a 94.42% chance of surviving 76 days or more after OTS. The differences in K-M survivor curves by GS indicated that GS 4 presented a lower survival function (Mantel-Cox test, p = 0.024; Wilcoxon-Breslow test, p = 0.044; Tarone-Ware test, p = 0.032). One of the best models to determine the association with mortality was the age-adjusted model for GS, high blood pressure, and respiratory history, with a hazard ratio of 1.112 in Cox regression analysis (95% CI: 1.005-1.230) and a sub hazard ratio of 1.111 (95% CI: 1.046-1.177) in Fine-Gray regression analysis for competitive risk. CONCLUSIONS: The infection risk after OTS was similar to that of the general population in a community transmission area; the grade of surgical aggression did not influence this rate. The survival probability was extremely high if patients had not previously been infected. With higher grades of surgical aggression, the risk of mortality was higher in OTS patients. Adjusting for age and other confounders (e.g., GS, high blood pressure and respiratory history) was associated with higher mortality rates. |*COVID-19[MESH] |Aggression[MESH] |COVID-19 Testing[MESH] |Humans[MESH] |SARS-CoV-2[MESH]Survival analysis and influence of the surgical aggression of a cohort(epmc).pdf DeepDyve Pubget Overpricing