ObjectiveTo analyze the characteristics of colorectal cancer surgery in the current version of Database from Colorectal Cancer (DACCA).MethodsThe DACCA version selected for this data analysis was the updated version on April 16th, 2020. The data items included timing of operation, types of operative procedure, radical resection level of operation, patient’s wish of anus-reserving, types of stomy, date of stoma closure, surgical approaches, extended resection, and type of intersphincteric resection (ISR). The data item interval of stoma closure was added, and the selected data items were statistically analyzed.ResultsThe total number of medical records (data rows) that met the criteria was 11 757, including 2 729 valid data on the timing of operation (23.2%), 11 389 valid data on the types of operative procedure (96.9%), 4 255 valid data on the radical resection level of operation (36.2%), 3 803 valid data on patient’s wish of anus-reserving (32.3%), 4 377 valid data on types of stomy (37.2%), 989 valid data on date of stoma closure (8.4%), 4 418 valid data on surgical approaches (37.6%), 3 941 valid data on extended resection (33.5%), and 1 156 valid data on type of ISR (9.8%). In the timing of operation, the most cases were performed immediately after discovery or neoadjuvant completion (915, 33.5%). In types of operative procedure, ultra low anterior resection (ULAR), right hemicolectomy (RHC), and low anterior resection (LAR) were the most, including 1 986 (17.4%), 1 412 (12.4%), and 1 041 (9.1%) lines. Respectively in the colon and rectal cancer surgery, the proportion of RHC (50.0%) and ULAR (26.0%) was the highest, with 172 (26.1%) and 815 (27.9%) extended resection. In ISR surgery the majority was ISR-2 (741, 64.1%). In radical resection level of operation, the number of R0 was the largest with 2 575 (60.5%) lines. In patient’s wish of anus-reserving, positive and rational were the most with 1 811 (47.6%) and 1 440 (37.9%) lines, respectively. And in types of stomy, there were 2 628 lines (60.0%) without stoma and 1 749 cases (40.0%) with stoma, among which the most lines were right lower ileum stoma (612, 35.0%). The minimum value, maximum value, and median value of interval of stoma closure were 0 d, 2 678 d and 112 d. The linear regression prediction of date of stoma closure by year was \begin{document}${\hat {y}} $\end{document}=9.234 3x+22.394 (R2=0.2928, P=0.07). In the surgical approaches, the majority was standard with 3 182 (72.0%) lines.ConclusionsIn the DACCA, rectal cancer surgery is still the majority, and ULAR is the most type. The application of extended resection in both colon and rectal cancer has important significance. The data related to stoma are diversified and need to be further studied.
ObjectiveTo analyze the tumor characteristics of colorectal cancer in the current version of Database from Colorectal Cancer (DACCA).MethodsThe DACCA version was the updated version on April 16, 2020. The data items including: procedure of anastomosis, shape of anastomosis, enhanced suture for anastomosis, stuffing, drainage, coverage of major omentum, anti-adhesion material, reconstruction of pelvic peritoneum, contaminate, and drug implants were analyzed for the characteristics of each selected data item.ResultsA total of 6 338 analyzable data rows were obtained by screening the DACCA database. Among the 6 338 pieces of data, the most common one was the double staple technique (58.1%), end-to-end anastomosis (69.4%), one-total-circle of enhancement (33.2%), and without stuffing (54.1%) in the items of procedure of anastomosis, shape of anastomosis, enhanced suture for anastomosis, stuffing, respectively; the ratio with drainage was higher (79.2%) in the term of drainage, the drainage time was (3.74±2.89) d and median drainage time was 3.00 d; the ratio with covering part of major omentum, without anti-adhesion material, with unilateral partial closure, without contaminate, and without drug implants were more higher, which was 41.1%, 79.8%, 58.7%, 73.9%, and 53.9% in the items of coverage of major omentum, anti-adhesion material, reconstruction of pelvic peritoneum, contaminate, and drug implants, respectively.ConclusionIt might better explain the outcome of surgery associated with intraoperative operation by studying the features of surgery of DACCA and guide the operation in the future for better outcomes.
ObjectiveTo describe the constructive process of neoadjuvant therapy for colorectal cancer part in the West China Colorectal Cancer Database (DACCA).MethodWe used the form of text description.ResultsThe specific concept of neoadjuvant therapy for colorectal cancer including neoadjuvant treatment therapies, compliance of patients with neoadjuvant therapy, neoadjuvant therapy intensity scheme, the CEA value of patients during neoadjuvant therapy, changes of symptoms, changes of primary tumor size in colorectal cancer, and TRG grading of the DACCA in the West China Hospital were defined. Then the neoadjuvant therapies were detailed for their definition, label, structure, error correction, and update.ConclusionThrough detailed description and specification of neoadjuvant therapy for colorectal cancer in DACCA in West China Hospital, it can provide a reference for the standardized treatment of colorectal cancer and also provide experiences for the peers who wish to build a colorectal cancer database.
ObjectiveTo analyze the neoadjuvant therapy of colorectal cancer in this center in the background of real world data by studying Database from Colorectal Cancer (DACCA) in West China Hospital of Sichuan University.MethodsData was selected from DACCA who was updated on August 15, 2019. After deleting duplicate value, patients whose tumor location and tumor pathologic characteristic showed colon or rectum, as well as adenocarcinoma, mucinous adenocarcinoma, and signet ring cell carcinoma were enrolled.ResultsThere were 2 783, 2 789, 2 790, 2 811, 4 148,3 824, 4 191, 3 676, 4 090, and 499 valid data of T, N, and M stages, clinical stages, tumor site, distance from tumor to anal dentate line, tumor pathologic characteristics, degree of tumor differentiation, neoadjuvant therapy, and compliance, respectively. There were 1 839 lines that " nature of the tumor pathology” was not empty and neoadjuvant scheme for the pure chemotherapy, radiotherapy alone or radiation, and chemotherapy, including 50 lines of signet ring cell carcinoma (2.7%), 299 lines of mucous adenocarcinoma (16.3%), 1 490 lines of adenocarcinoma (81.0%), various kinds of pathology in selection of neoadjuvant therapy difference was statistically significant (χ2=9.138, P=0.041). Except for the data lines with null value in the column of " operation date”, there were 2 234 (82.1%) and 486 (17.9%) effective data lines of " recommended” and " not recommended” for the use of neoadjuvant therapy, respectively. In the years with a large amount of data, among the patients who completed neoadjuvant therapy, the proportion of patients meeting the recommended indications was 27.4%–67.6%, with an average of 47.4%. Patients who did not meet the recommended indications but were recommended (off-label use) accounted for 7.3%–70.0%, with an average of 39.8%. According to regression analysis, the proportion in line with the recommendation (\begin{document}$\hat y $\end{document}=–0.032 5x+66.003 2, P=0.020) varies with the year, and the overall trend shows a gradual decline. The proportion of the use of super indications (\begin{document}$\hat y $\end{document}=–0.054 5x+110.174 6, P=0.002) changed with the year, and the overall trend showed a decline. A total of 1 161 valid data with non-null values of " eoadjuvant therapy regimen” and " recommended or not recommended” showed statistically significant difference in the use rate of neoadjuvant therapy among patients with different recommendation groups (χ2=9.244, P=0.002). " Patient compliance” was shown as " active cooperation” and " passive acceptance”, and " neoadjuvant therapy” was shown as " radiotherapy alone”" chemotherapy alone”, and " chemoradiotherapy” were 470 lines. There was no statistically significant difference in neoadjuvant therapy between patients receiving active and passive treatment (χ2=0.537, P=0.841). The effective data of clinical remission degree meeting the research conditions were 388 lines, including 121 lines of complete response (31.2%), 180 lines of partial response (46.4%), 79 lines of stable disease (20.4%), and 8 lines of progressive disease (2.1%). There was no statistically significant difference in clinical response degree among patients with different neoadjuvant therapy (H=0.435, P=0.783). There were 346 lines with effective data of pathologic tumor regression grade (TRG) meeting the study conditions, including 47 lines with TRG0 (13.6%), 39 lines with TRG1 (11.3%), 180 lines with TRG2 (52.0%), and 80 lines with TRG3 (23.1%). There was no statistical difference in the degree of TRG among patients with different neoadjuvant therapy (H=1.816, P=0.518).ConclusionsThe real world study reflects that in the western regional medical center, the demand for neoadjuvant therapy among the patients with colorectal cancer covered is huge. Although the implementation of neoadjuvant therapy is greatly influenced by the doctor’s recommendation behavior, the selection and recommendation of neoadjuvant therapy according to some specific clinical application guidelines are not fully met. The impact of more behavioral factors requires further in-depth analysis and research.
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.