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.
ObjectiveTo elaborate constitute, definition, and interpretation of stage module of colorectal cancer in the Database from Colorectal Cancer (DACCA) in the West China Hospital.MethodThe article was described in the words.ResultsIn the DACCA, the columns were selected by the colorectal cancer staging module. The overall stages included: the stage during surgery, cpi comprehensive stage, and TNM stage. The classified stages included: the T, N, and M stages of pathology, clinical, and imaging; The risk factors included the cancerous contamination and high-risk factors. Then these items were subdivided and detailed for their definition, form, label and structure, error correction and update, and how to be used in the analysis of data in the DACCA.ConclusionsThrough detailed description and specification of current stage module of colorectal cancer in DACCA in West China Hospital, it can provide a reference for standardized treatment of colorectal cancer and also provide experiences for the peers who wish to build a colorectal cancer database.
Objective To analyze the impact of body mass index (BMI) on tumor characteristics of colorectal patients served by West China Hospital as a regional center in the current version of Database from Colorectal Cancer (DACCA). MethodsThe data of DACCA was updated on October 16, 2021. All data items included BMI, precancerous lesions, cancer family, tumor site, tumor morphology, location, differentiation, pathological properties of tumor, obstruction, overlap, perforation, pain, edema, and bleeding. The patients were divided into lean (BMI<18.5 kg/m2), normal (BMI 18.5–23.9 kg/m2), overweight (BMI 24.0–27.9 kg/m2) and obesity (BMI≥28.0 kg/m2) by Chinese classification methods. ResultsAfter scanning, 5 761 data rows were analyzed. Chi-square test showed that there was significant difference in the type composition ratio of tumor location in colorectal cancer patients under different BMI groups (χ2=31.477, P<0.001). Rank sum test showed that there was significant difference in the degree of obstruction (H=42.490, P<0.001), intussusception (H=8.179, P=0.042), edema (H=14.795, P=0.002), and bleeding (H=9.884, P=0.020) among different BMI groups. ConclusionsThe BMI classification of colorectal cancer patients is related to the location of tumor and the occurrence of some tumor complications. Patients with tumor involving intestinal lumens for one week are more likely to have low BMI. The patients with low BMI are more likely to have severe bleeding, obstruction, intestinal intussusception, and severe intestinal wall edema.
ObjectiveTo investigate the prognostic factors of primary gastric squamous cell carcinoma (SCC) and develop a nomogram for predicting the survival of gastric SCC.MethodsData of 199 cases of primary gastric SCC from 2004 to 2015 were collected in the National Cancer Institute SEER database by SEER Stat 8.3.5 software. X-tile software was used to determine the best cut-off value of the age, SPSS 25.0 software was used to analyze the prognostic factors of gastric SCC and draw a Kaplan-Meier curve, and then the Cox proportional hazard regression model analysis was performed to obtain independent prognostic factors of gastric SCC. We used R studio software to visualize the model and draw a nomogram. C-index was used to evaluate the prediction effect of the nomogram. Bootstrap analyses with 1 000 resamples were applied to complete the internal verification of the nomogram.ResultsAmong the 199 patients, survival rates for 1-, 3-, and 5-year were 40.7%, 22.4%, and 15.4%, respectively. Age (χ2=6.886, P=0.009), primary site (χ2=14.918, P=0.037), race (χ2=7.668, P=0.022), surgery (χ2=16.523, P<0.001), histologic type (χ2=9.372, P=0.009), T stage (χ2=11.639, P=0.009), and M stage (χ2=31.091, P<0.001) had a significant correlation with survival time of patients. The results of the Cox proportional hazard regression model showed that, age [HR=1.831, 95%CI was (1.289, 2.601)], primary site [HR=1.105, 95%CI was (1.019, 1.199)], M stage [HR=2.222, 95%CI was (1.552, 3.179)], and surgery [HR=0.561, 95%CI was (0.377, 0.835)] were independent prognostic factors affecting the survival of gastric SCC. Four independent prognostic factors contributed to constructing a nomogram with a C-index of 0.700.ConclusionIn this research, a reliable predictive model is constructed and drawn into a nomogram, which can be used for clinical reference.
ObjectiveTo analyze the staging methods of colorectal cancer data in the current version of the Database from Colorectal Cancer (DACCA).MethodsThe DACCA version selected for this data analysis was updated at April 16th, 2020. The columns included stage during surgery, comprehensive stage of clinical, pathologic and imaging (cpi comprehensive stage), TNM stage, pathologic T stage, imaging T stage, nerves involvement, pathologic anus stage, clinical anus stage, imaging anus stage, pathologic mesentery stage, clinical mesentery stage, imaging mesentery stage, pathologic N stage, imaging N stage, positive lymph nodes ratio, cancerous nodules, M stage, cancerous emboli, pathologic vessel stage, clinical vessel stage, imaging vessel stage, cancerous contamination, and high-risk factors. Extracted data were statistically analyzed.ResultsThe total number of data medical records (data rows) that met the criteria was 6 474, the valid data of TNM stage was 4 511 (69.7%), the valid data of stage during surgery was 5 684 (87.8%), and the valid data of cpi comprehensive stage was 4 045 (62.5%). 1 540 data (41.6%) were consistent with stage during surgery and TNM stage, and 2 884 data (76.7%) were consistent with cpi comprehensive stage and TNM stage. According to the data of T, N, and M stage, the proportion of patients with pathologic T4a stage was the highest (40.5%), followed by T3 stage (24.8%); the most T4a stage (31.9%) on the image, followed by T4b stage (28.7%). The pathologic N stage with lymph node metastasis was about 41.9% (N1 and N2), and the imaging N stage lymph node metastasis was about 51.4%. There were a total of 4 745 valid data in the M stage (73.3%). There were 4 313 valid data in the nerves involvement (66.7%), suspected involvement and confirmed involvement, were 691 (16.0%) and 253 (5.9%) respectively. The valid data of anal pathology, clinical, and imaging stage were 4 115 (63.6%), 599 (9.3%), and 598 (9.2%), and only 30 (0.7%), 8 (1.3%), and 13 (2.2%) on muscle involvement respectively. The valid data of pathologic, clinical, and imaging mesentery stage were 732 (11.3%), 589 (9.1%), and 592 (9.1%). There were 4 458 (68.9%) valid data of positive lymph nodes ratio, and 2 908 (44.9%) valid data of cancerous nodules. There were 4 286 valid data of cancerous emboli (66.2%). A total of 244 data (41.1%) of increased blood vessels around tumors in the imaging vessel stage, 274 data (46.4%) of that in clinical vessel stage, and only 1 063 (27.7%) of pathologic vessel stage. There were 3 865 valid data (59.7%) of the cancerous contamination, and the proportion of the third level (746/2 753, 27.1%) in the high-risk factors was the highest.ConclusionThrough detailed analysis of the DACCA database, it is hoped that a more complete and accurate evaluation system of tumor severity can be established, and high-risk factors can provide some ideas for judging prognosis.
Objective To establish a prediction model for the 1-, 3-, and 5-year survival rates in patients with gastric cancer liver metastases (GCLM) by analyzing prognostic factors based on the Surveillance, Epidemiology, and End Results (SEER) database. Methods Clinical and pathological data from 591 patients diagnosed with GCLM between 2010 and 2015 were obtained from the SEER database. The population was randomly divided into a training cohort and an internal validation cohort at a 7 to 3 ratio. Independent predictors of GCLM were analyzed using univariate and multifactorial Cox regression. Consequently, nomograms were constructed. The model's accuracy was verified by calibration curve, ROC curve, and the C-index, and the clinical utility of the model was analyzed through decision curve analysis. Results Tumor differentiation grade, surgical status, and chemotherapy were significantly associated with the prognosis of GCLM patients, and these three factors were included in constructing the prognostic model and plotting the nomogram. The C-index was 0.706 (95%CI 0.677 to 0.735) and 0.749 (95%CI 0.710 to 0.788) for the training set and the internal validation cohort, respectively. The results of the ROC curve analysis indicated that the area under the curve (AUC) was over 0.7 at 1, 3, and 5 years for both the training and validation cohorts. Conclusion The prediction model of the GCLM is developed based on the 3 factors, i.e., tumor differentiation grade, surgery, and chemotherapy, and shows good prediction accuracy and thus may promote clinical decision making and individualized treatment of GCLM patients.
objective To analyze the impact of body mass index (BMI) on medical and surgical complications of colorectal cancer patients served by West China Hospital, based on the current version of Database from Colorectal Cancer (DACCA). Methods The data of DACCA was updated on September 27, 2021. The data included BMI, surgical complications, liver nodules, liver function, renal nodules, renal function, operation history, medical complications, diabetes, hypertension, pneumonia, pulmonary nodules, pulmonary function, heart disease, thrombosis, and cardiac function. Results After scanning, 5 305 data rows were included. BMI was divided by Chinese four classification methods. The analysis results showed that in terms of surgical complications, obese patients were more likely to be complicated with surgical complications of digestive system (χ2= 43.883, P<0.001) and reproductive system (χ2=13.139, P=0.004). Lean patients were more likely to have surgical complications of urinary system (χ2=223.415, P<0.001), and obese patients had liver function (H=61.521, P<0.001) and renal function (H=9.994, P=0.019) might be even worse. In terms of operation history, BMI in colorectal cancer patients had nothing to do with the number of times of operation (H=6.262, P=0.100), and operation history of each system or department (P>0.05). Regarding to medical complications, with the increase of BMI, the risk of colorectal cancer patients with diabetes mellitus (χ2=118.597, P<0.001), or hypertension (χ2= 163.334, P< 0.001) increased. Patients with low BMI were more likely to have pneumonia (H=7.899, P= 0.048) and worse pulmonary function (H=40.673, P<0.001). Conclusions The analysis results of DACCA database show that BMI is not related to the occurrence of any special surgical history included in the research. Because the internal and external complications of patients are closely related to the treatment plan and prognosis, we should pay more attention to the obese patients in the process of clinical treatment, and they are more likely to have multisystemic abnormalities and various abnormal indicators than other patients. For thin patients, we should pay more attention to their lung function and inflammatory lesions, so as to improve the clinical therapeutic effect.
ObjectiveTo analyze the relationship between occupation and tumor characteristics of colorectal patients served by West China Hospital of Sichuan University as a regional center in the current version of Database from Colorectal Cancer (DACCA). MethodsThe data of DACCA was updated on January 5, 2022. All data items included occupation, tumor morphology, distance of tumor from dentate line, tumor site, properties of tumor, differentiation degree, postoperative complex physiological index (CPI) stage, tumor comorbidities, tumor location, and tumor occurrence. According to the 2015 edition of the Occupational Classification of the People’s Republic of China, the occupational parameters of patients in this study were divided into three groups: Mental workers, physical workers and unemployed residents. ResultsThe DACCA database was filtered according to the conditions, obtaining 3 215 valid data. In terms of tumor complications, there were significant differences in the proportion of tumor bleeding, perforation grade, mechanical intestinal obstruction degree and pain degree among the different occupational groups (P<0.05). There were no significant difference in the ratio of edema degree and intussusception of tumor site among the different occupational groups (P>0.05). There were no significant difference in the composition ratio of tumor differentiation degree, tumor occurrence, tumor orientation and tumor morphology among the different occupational groups (P>0.05). The composition ratio of CPI staging of colorectal cancer, the distance between tumor and dentate line, the composition ratio of different tumor pathological properties, and the composition ratio of tumor located in rectum and colon were statistically significant (P<0.05). ConclusionPreoperative tumor characteristics of patients with colorectal cancer are associated with various occupations. In patients with rectal cancer, the distance from the dentate line to the physical work of the tumor is smaller, lower site, some tumor complications are more severe, and the stage is relatively later.
ObjectiveTo verify the influence of different variable selection methods on the performance of clinical prediction models. MethodsThree sample sets were extracted from the MIMIC database (acute myocardial infarction group, sepsis group, and cerebral hemorrhage group) using the direct entry of COX regression, step by step forward, step by step backward, LASSO, and ridge regression, based on random forest. These existing six methods of variable importance algorithm, and the optimal variable set of different selected methods were used to construct the model. Through the C index, the area under the ROC curve (AUC value) and the calibration curve, and the results within and between groups were compared. ResultsThe variables and numbers selected by the six variable selection methods were different, however, whether it was within or between groups did not reflect which method had the advantage of significantly improving the performance of the model. ConclusionsPrior to using the variable selection method to establish a clinical prediction model, we should first clarify the research purpose and determine the type of data. Combining medical knowledge to select a method that can meet the data type and simultaneously achieve the research purpose.
Objective To predict the patients who can benefit from local surgery for bone-only metastatic breast cancer (bMBC). Methods Patients newly diagnosed with bMBC between 2010 and 2019 in SEER database were randomly divided into a training set and a validation set at a ratio of 7∶3. The Cox proportional hazards model was used to analyze the independent prognostic factors of overall survival in the training set, and the variables were screened and the prognostic prediction model was constructed. The concordance index (C-index), time-dependent clinical receiver operating characteristic curve and area under the curve (AUC), calibration curve and decision curve analysis (DCA) were used to evaluate the discrimination, calibration and clinical applicability of the model in the training set and validation set, respectively. The model was used to calculate the patient risk score and classify the patients into low-, medium- and high-risk groups. Survival analysis was used to compare the survival difference between surgical and non-surgical patients in different risk groups. Results A total of 2057 patients were enrolled with a median age of 45 years (interquartile range 47-62 years) and a median follow-up of 32 months (interquartile range 16-53 months). Totally 865 patients (42.1%) died. Multivariate Cox proportional hazards model analysis showed that the overall survival of patients with surgery was better than that of patients without surgery [hazard ratio=0.51, 95% confidence interval (0.43, 0.60), P<0.001]. Chemotherapy, marital status, molecular subtype, age, pathological type and histological grade were independent prognostic factors for overall survival (P<0.05), and a prognostic prediction model was constructed based on the independent prognostic factors. The C-index was 0.702 in the training set and 0.703 in the validation set. The 1-, 3-, and 5-year AUCs of the training set and validation set were 0.734, 0.727, 0.731 and 0.755, 0.737, 0.708, respectively. The calibration curve showed that the predicted survival rates of 1, 3, and 5 years in the training set and the validation set were highly consistent with the actual survival rates. DCA showed that the prediction model had certain clinical applicability in the training set and the validation set. Patients were divided into low-, medium- and high-risk subgroups according to their risk scores. The results of log-rank test showed that local surgery improved overall survival in the low-risk group (training set: P=0.013; validation set: P=0.024), but local surgery did not improve overall survival in the medium-risk group (training set: P=0.45; validation set: P=0.77) or high-risk group (training set: P=0.56; validation set: P=0.94). Conclusions Local surgery can improve the overall survival of some patients with newly diagnosed bMBC. The prognostic stratification model based on clinicopathological features can evaluate the benefit of local surgery in patients with newly diagnosed bMBC.