SUMSearch and TRIP database are meta search engines for searching clinical evidence. This article introduces major contents and search methods of the SUMSearch and TRIP database, so as to provide quick search resources and technical help for evidence-based practice.
“Patient profile” is a specific application of user profile technology in the field of healthcare. As an emerging means of integrating health information, it provides personalized and precise health management for patients by analyzing multidimensional health data, improving health management effectiveness, reducing medical costs, and increasing their satisfaction and participation. It has broad application prospects in the field of nursing, but the current research status of its application in the field of nursing is not clear. This article reviews the application progress of patient profile based on big data in the field of nursing at home and abroad, systematically analyzes its construction methods, application scenarios, implementation effects and challenges, and puts forward relevant suggestions, aiming to provide references for the precise and intelligent development of nursing services.
ObjectiveTo analyze the risk factors for early mortality in patients with stage Ⅳ colorectal cancer, and further construct and validate Nomogram prediction model for early mortality in stage Ⅳ colorectal cancer. MethodsA retrospective analysis was conducted on the clinical and pathological data of stage Ⅳ colorectal cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database in the United States from 2018 to 2020. The study data was randomly divided into a training cohort and a validation cohort at a ratio of 8∶2. Multivariate logistic regression analysis was performed in the training cohort to screen for risk factors for early mortality in stage Ⅳ colorectal cancer patients, and Nomogram prediction model was further constructed. Receiver operating characteristic curve (ROC), calibration curve, and clinical decision curve analysis (DCA) were plotted. ResultsAge (50–70 group, OR=1.984, P=0.007; >70 group, OR=1.997, P=0.008), unmarried (OR=1.342, P=0.025), primary tumor differentiation of G3+G4 (OR=1.817, P<0.001), T4 stage (OR=1.434, P=0.009), N2 stage (OR=1.621, P<0.001), M1c stage (OR=1.439, P=0.036), no chemotherapy (OR=21.820, P<0.001), bone metastasis (OR=2.000, P=0.042), brain metastasis (OR=6.715, P=0.001) and liver metastasis (OR=1.886, P<0.001) were risk factors for all-cause early death in stage Ⅳ colorectal cancer patients. Age（50–70 group, OR=2.025, P=0.008; >70 group, OR=1.925, P=0.017), primary tumor differentiation grade of G3+G4 (OR=1.818, P<0.001), T4 stage (OR=1.424, P=0.013), N2 stage (OR=1.637, P<0.001), M1c stage (OR=1.541, P=0.016), no chemotherapy (OR=21.832, P<0.001), brain metastasis (OR=6.089, P=0.001), liver metastasis (OR=2.100, P<0.001) were factors for cancer-specific early death of stages Ⅳ colorectal cancer patients. Based on these variables, we constructed two Nomogram prediction models for all-cause early death and cancer-specific early death in stage Ⅳ colorectal cancer patients. The area under curve (AUC) value of the all-cause early death prediction model in the training queue was 0.874 [95% CI (0.855, 0.893)], and the AUC value of the cancer specific early death prediction model was 0.874 [95%CI (0.855, 0.894)]; the AUC value of the all-cause early death prediction model in the validation queue was 0.868 [95%CI (0.829, 0.907)], and the AUC value of the cancer specific early death prediction model was 0.867 [95%CI (0.827, 0.907)], indicating that the model had good predictive ability. The calibration curve showed that the predictive models had good consistency with the actual results for predicting early mortality in stage Ⅳ colorectal cancer, and the DCA curve showed that the models could provide patients with higher clinical benefits. ConclusionThe predictive models established in this study have good predictive performance for early mortality in stage Ⅳ colorectal cancer patients, which is helpful for clinical physicians to identify high-risk patients in the early stage and develop personalized treatment plans in clinical practice.
Early screening is an important means to reduce breast cancer mortality. In order to solve the problem of low breast cancer screening rates caused by limited medical resources in remote and impoverished areas, this paper designs a breast cancer screening system aided with portable ultrasound Clarius. The system automatically segments the tumor area of the B-ultrasound image on the mobile terminal and uses the ultrasound radio frequency data on the cloud server to automatically classify the benign and malignant tumors. Experimental results in this study show that the accuracy of breast tumor segmentation reaches 98%, and the accuracy of benign and malignant classification reaches 82%, and the system is accurate and reliable. The system is easy to set up and operate, which is convenient for patients in remote and poor areas to carry out early breast cancer screening. It is beneficial to objectively diagnose disease, and it is the first time for the domestic breast cancer auxiliary screening system on the mobile terminal.
To enhance the quality and transparency of oncology real-world evidence studies, the European Society for Medical Oncology (ESMO) has developed the first specific reporting guidelines for oncology RWE studies in peer-reviewed journals "the ESMO Guidance for Reporting Oncology Real-World Evidence (GROW)". To facilitate readers understanding and application of these reporting standards, this article introduces and interprets the development process and main contents of the ESMO-GROW checklist.
Hazard ratio (HR) is usually regarded as the effect size in survival studies. Meanwhile, it is supposed to be perfect for pooling results in the meta-analysis of survival data. However, it does not function usually due to absence of original data for pooling HR. As a compromise method, entering data from reading Kaplan-Meier curves and follow-up times into the calculation spreadsheet can also be used to obtain related survival data. But related study on the subject is scarce, and opinions are inconsistent. Accordingly, we conduct this study to further illustrate the procedure in details.
ObjectiveTo understand the impact of preoperative nutritional status on the postoperative complications for patients with low/ultra-low rectal cancer undergoing extreme sphincter-preserving surgery following neoadjuvant therapy. MethodsThe patients with low/ultra-low rectal cancer who underwent extreme sphincter-preserving surgery following neoadjuvant therapy from January 2009 to December 2020 were retrospectively collected using the Database from Colorectal Cancer (DACCA), and then who were assigned into a nutritional risk group (the score was low than 3 by the Nutrition Risk Screening 2002) and non-nutritional risk group (the score was 3 or more by the Nutrition Risk Screening 2002). The postoperative complications and survival were analyzed for the patients with or without nutritional risk. The postoperative complications were defined as early-term (complications occurring within 30 d after surgery), middle-term (complications occurring during 30–180 d after surgery), and long-term (complications occurring at 180 d and more after surgery). The survival indicators included overall survival and disease-specific survival. ResultsA total of 680 patients who met the inclusion criteria for this study were retrieved from the DACCA database. Among them, there were 500 (73.5%) patients without nutritional risk and 180 (26.5%) patients with nutritional risk. The postoperative follow-up time was 0–152 months (with average 48.9 months). Five hundreds and forty-three survived, including 471 (86.7%) patients with free-tumors survival and 72 (13.3%) patients with tumors survival. There were 137 deaths, including 122 (89.1%) patients with cancer related deaths and 15 (10.9%) patients with non-cancer related deaths. There were 48 (7.1%) cases of early-term postoperative complications, 51 (7.5%) cases of middle-term complications, and 17 (2.5%) cases of long-term complications. There were no statistical differences in the incidence of overall complications between the patients with and without nutritional risk (χ2=3.749, P=0.053; χ2=2.205, P=0.138; χ2=310, P=0.578). The specific complications at different stages after surgery (excluding the anastomotic leakage complications in the patients with nutritional risk was higher in patients without nutritional risk, P=0.034) had no statistical differences between the two groups (P>0.05). The survival curves (overall survival and disease-specific survival) using the Kaplan-Meier method had no statistical differences between the patients with and without nutritional risk (χ2=3.316, P=0.069; χ2=3.712, P=0.054). ConclusionsFrom the analysis results of this study, for the rectal cancer patients who underwent extreme sphincter-preserving surgery following neoadjuvant therapy, the patients with preoperative nutritional risk are more prone to anastomotic leakage within 30 d after surgery. Although other postoperative complications and long-term survival outcomes have no statistical differences between patients with and without nutritional risk, preoperative nutritional management for them cannot be ignored.
The minimally invasive cardiovascular surgery developed rapidly in last decades. In order to promote the development of minimally invasive cardiovascular surgery in China, the Chinese Minimally Invasive Cardiovascular Surgery Committee (CMICS) has gradually standardized the collection and report of the data of Chinese minimally invasive cardiovascular surgery since its establishment. The total operation volume of minimally invasive cardiovascular surgery in China has achieved substantial growth with a remarkable popularization of concepts of minimally invasive medicine in 2019. The data of Chinese minimally invasive cardiovascular surgery in 2019 was reported as a paper for the first time, which may provide reference to cardiovascular surgeons and related professionals.
when we conducted a meta-analysis, it is often an annoying thing to deal with the data of discrete exposure and multiple outcomes. Conventional "high VS low" approach abandoned the information of middle category, and led to the loss of statistical power. In this paper, we introduced a method and software to combine the groups of discrete exposure and multiple outcomes in the meta-analysis of epidemiological studies. Firstly, we introduced the transforming and combination theory and method, and then, we conducted the combination using EXCEL macro software. The result was consistent with the results of the original data in the combination of discrete exposure and multiple outcome data. Therefore, in the case of the original research data cannot be acquired, EXCEL macro software can be a good solution.
ObjectiveTo analyze the details and efficacy of neoadjuvant therapy of colorectal cancer in the current version of Database from Colorectal Cancer (DACCA).MethodsThe DACCA version selected for this data analysis was the updated version on July 28th, 2020. The data items included “planned strategy of neoadjuvant therapy” “compliance of neoadjuvant therapy”, and “cycles of neoadjuvant therapy”. Item of “planned strategy of neoadjuvant therapy” included “accuracy of neoadjuvant therapy” and “once included in researches”. Item of “the intensity of neoadjuvant therapy” included “chemotherapy” “cycles of neoadjuvant therapy” “targeted drugs”, and “neoadjuvant radiotherapy”. Item of “effect of neoadjuvant therapy” included CEA value of “pre-neoadjuvant therapy” and “post-neoadjuvant therapy”“variation of tumor markers” “variation of symptom” “variation of gross” “variation of radiography”, and tumor regression grade (TRG). The selected data items were statistically analyzed.ResultsThe total number of medical records (data rows) that met the criteria was 7 513, including 2 539 (33.8%) valid data on the “accuracy of neoadjuvant therapy”, 498 (6.6%) valid data on “once included in researches”, 637 (8.5%) valid data on the “compliance of neoadjuvant therapy”, 2 077 (27.6%) valid data on “neoadjuvant chemotherapy”, 614 (8.2%) valid data on “cycles of neoadjuvant therapy”, 455 (6.1%) valid data on “targeted drugs”, 135 (1.8%) valid data on “neoadjuvant radiotherapy”, 5 022 (66.8%) valid data on “pre-neoadjuvant therapy CEA value”, 818 (10.9%) valid data on “post-neoadjuvant therapy CEA value ”, 614 (8.2%) valid data on “variation of tumor marker”, 464 (6.2%) valid data on “variation of symptom”, 478 (6.4%) valid data on “variation of gross”, 492 (6.5%) valid data on “variation of radiography”, and 459 (6.1%) valid data on TRG. During the correlation analysis, it appeared that “variation of tumor marker” and “variation of gross” (χ2=6.26, P=0.02), “variation of symptom” and “variation of gross”, “radiography” and TRG (χ2=53.71, P<0.01; χ2=38.41, P<0.01; χ2=8.68, P<0.01), “variation of gross” and “variation of radiography”, and TRG (χ2=44.41, P<0.01; χ2=100.37, P<0.01), “variation of radiography” and TRG (χ2=31.52, P<0.01) were related with each other.ConclusionsThe protocol choosing of neoadjuvant therapy has a room for further research and DACCA can provide data support for those who is willing to perform neoadjuvant therapy. The efficacy indicators of neoadjuvant therapy have association with each other, the better understand of it will provide more valuable information for the establishment of therapeutic prediction model.