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首页> 外文期刊>BMC Musculoskeletal Disorders >Survival analysis and influence of the surgical aggression of a cohort of orthopedic and trauma patients in a non-controlled spread COVID-19 scenario
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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

机译：在非控制的展开Covid-19场景中骨科和创伤患者队队和创伤患者的外科侵略的生存分析及影响

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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. 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. 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. 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.
机译：在不受控制的Covid-19传播期间确定经过骨科和创伤手术（OTS）的健康患者的感染率和死亡率可能有助于为未来波浪的准备提供帮助。本研究在非感染的OTS患者队列中进行了生存分析，并确定了Covid-19对死亡率的影响。该观察项研究包括184名患者在手术活动停止和实施特别措施之前，在手术活动前一月接受OTS的患者。基于手术的位置和产生的炎症等级建立了四组手术（GS）。评估了感染和感染率的粗暴风险。分析了GS的存活率和失效功能。评估了GS的Kaplan-Meier生存曲线的比较。 Cox回归和细灰色模型用于确定不同混淆对死亡率的影响。 Covid-19诊断的原始风险为14.13％（95％CI：9.83-19.90％）。总发生率为2.67（1000人 - 天，95％CI：1.74-3.91）。在随访结束时，在OTS后有94.42％的几率幸存76？天或更长时间。 GS的KM Survivor曲线的差异表明GS 4呈现出较低的存活功能（Mantel-Cox测试，P？= 0.024; Wilcoxon-Breeslow测试，P？= 0.044; Tarone-Ware测试，P？= 0.032 ）。确定与死亡率相关的最佳模型之一是GS，高血压和呼吸史的年龄调整的模型，危险比在COX回归分析中为1.112（95％CI：1.005-1.230）和亚竞争风险的细灰色回归分析中1.111（95％CI：1.046-1.177）的危险比。 OTS后的感染风险与社区传输区域的一般人群相似;手术侵略等级没有影响这个速度。如果患者以前没有被感染，则存活概率极高。具有较高等级的手术侵略，OTS患者的死亡风险较高。调整年龄和其他混乱（例如，GS，高血压和呼吸史）与更高的死亡率有关。

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