ObjectiveTo investigate the effect of timing of removal of drainage tube on complications after radical thyroidectomy by da Vinci robot.MethodsThree hundred and fifteen patients with thyroid cancer treated by da Vinci robot from July 2014 to December 2018 in our department were reviewed. The patients were divided into two groups according to the amount of drainage fluid at extubation: observation group (99 cases) and control group (216 cases). The extubation indication: in the observation group, the drainage volume was less than 20 mL for 24 hours in two days; in the control group, according to most clinical concepts, the drainage volume was less than 10 mL for 24 hours in two days. The infection rate of wound and tunnel, the incidence of hematoma, wound healing, the time of drainage tube removal and the time of hospitalization were observed.ResultsThere were no significant difference in infection rate, hematoma incidence and wound healing rate between the observation group and the control group (P>0.05). The postoperative extubation time and hospitalization time in the observation group was significantly shorter than that in the control group (P<0.05).ConclusionsAfter the radical operation of thyroid cancer by Leonardo da Vinci robot, taking the amount of wound drainage fluid less than 20 mL/24 hours for 2 days as the time of extubation does not increase the incidence of complications, but it can significantly shorten the time of extubation and hospitalization of patients, which can be widely used in clinical practice.
The incidence of acute kidney injury (AKI) has increased rapidly in recent years. The causes of AKI are complex and diverse, and there is no effective treatment strategy. Reliable and stable animal models and in vitro models play an important role in the development and prevention of AKI. Focusing on rodent models and in vitro models, this review summarizes AKI models induced by ischemia, nephrotoxic drugs and urinary tract obstruction from three levels of prerenal, intrinsic renal and postrenal AKI.
Objective To explore the correlation between the quantitative and qualitative features of CT images and the invasiveness of pulmonary ground-glass nodules, providing reference value for preoperative planning of patients with ground-glass nodules. MethodsThe patients with ground-glass nodules who underwent surgical treatment and were diagnosed with pulmonary adenocarcinoma from September 2020 to July 2022 at the Third Affiliated Hospital of Kunming Medical University were collected. Based on the pathological diagnosis results, they were divided into two groups: a non-invasive adenocarcinoma group with in situ and minimally invasive adenocarcinoma, and an invasive adenocarcinoma group. Imaging features were collected, and a univariate logistic regression analysis was conducted on the clinical and imaging data of the patients. Variables with statistical difference were selected for multivariate logistic regression analysis to establish a predictive model of invasive adenocarcinoma based on independent risk factors. Finally, the sensitivity and specificity were calculated based on the Youden index. Results A total of 555 patients were collected. The were 310 patients in the non-invasive adenocarcinoma group, including 235 females and 75 males, with a meadian age of 49 (43, 58) years, and 245 patients in the invasive adenocarcinoma group, including 163 females and 82 males, with a meadian age of 53 (46, 61) years. The binary logistic regression analysis showed that the maximum diameter (OR=4.707, 95%CI 2.060 to 10.758), consolidation/tumor ratio (CTR, OR=1.027, 95%CI 1.011 to 1.043), maximum CT value (OR=1.025, 95%CI 1.004 to 1.047), mean CT value (OR=1.035, 95%CI 1.008 to 1.063), spiculation sign (OR=2.055, 95%CI 1.148 to 3.679), and vascular convergence sign (OR=2.508, 95%CI 1.345 to 4.676) were independent risk factors for the occurrence of invasive adenocarcinoma (P<0.05). Based on the independent predictive factors, a predictive model of invasive adenocarcinoma was constructed. The formula for the model prediction was: Logit(P)=–1.293+1.549×maximum diameter of lesion+0.026×CTR+0.025×maximum CT value+0.034×mean CT value+0.72×spiculation sign+0.919×vascular convergence sign. The area under the receiver operating characteristic curve of the model was 0.910 (95%CI 0.885 to 0.934), indicating that the model had good discrimination ability. The calibration curve showed that the predictive model had good calibration, and the decision analysis curve showed that the model had good clinical utility. Conclusion The predictive model combining quantitative and qualitative features of CT has a good predictive ability for the invasiveness of ground-glass nodules. Its predictive performance is higher than any single indicator.