ObjectiveTo describe the constructive process of follow-up of colorectal cancer part in the Database from Colorectal Cancer (DACCA) in West China Hospital. MethodThe article was described in words. ResultsThe specific concepts of follow-up of colorectal cancer including end-stage of follow-up, survival status, follow-up strategy, follow-up emphasis, follow-up plan, follow-up record using communication tools, follow-up frequency, annual follow-up times, and single follow-up record of the DACCA in the West China Hospital were defined. Then they were detailed for their definition, label, structure, error correction, and update. ConclusionThrough the detailed description of the details of follow-up of colorectal cancer of DACCA in West China Hospital, it provides the standard and basis for the clinical application of DACCA in the future, and provides reference for other peers who wish to build a colorectal cancer database.
ObjectiveTo explain surgical and medical comorbidities and preoperative physical status of colorectal cancer in detail as well as their tags and structures of Database from Colorectal Cancer (DACCA) in West China Hospital.MethodThe article was described in words.ResultsThe definition to the surgical comorbidities with its related content module, the medical comorbidity with its related content modules, and the preoperative physical status and characteristics of the DACCA in West China Hospital were given. The data label corresponding to each item in the database and the structured way needed for the big data application stage in detail were explained. And the error correction notes for all classification items were described.ConclusionsThrough the detailed description of the medical and surgical comorbidities and the preoperative physical status of DACCA in West China Hospital, it provides the standard and basis for the clinical application of DACCA in the future, and provides reference for other peers who wish to build a colorectal cancer database.
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 follow-up data of colorectal cancer in the Database from Colorectal Cancer (DACCA).MethodsThe information in the Dacca database was screened, and the one whose operative date and follow-up date were not blank in the total data was selected. The follow-up data were analyzed, including length of follow-up, survival outcomes, coping styles (doctors’ attitude and reaction for follow-up), follow-up path (whether to choose out-patient, Wechat, QQ tools, phone call, text message, mobile application, face-to-face), the number of follow-up (the number of out-patient follow-up, the number of telephone follow-up, and the number of follow-up within 5 years).ResultsA total of 6 437 data items were analyzed for colorectal cancer adjuvant follow-up. ① The follow-up period of five years (2004–2015) was 56.6% (3 642/6 437), and the follow-up time was 0–201, 67 (26, 97) months. ② The highest data composition ratio of survival outcomes was “Survival” (79.7%, 4 611/5 787), and in the data with five-year follow-up period (2004–2015), the highest data composition ratio of survival outcomes was “Survival” (75.0%, 2 550/3 401), and the survival rate of the five-year follow-up period in 2008 was the highest (91.4%, 235/257). ③ The highest data composition ratio of the coping styles was the doctors’ active follow-up (76.8%, 2 121/2 762). ④ The highest data composition ratio of the follow-up path was out-patient service (90.6%, 4 236/4 676). ⑤ The highest data composition ratio of the number of out-patient follow-up was conducted by the original surgical team (100%, 4 380/4 380), the specific number was 0–130、5 (2, 10) times. The data composition ratio of telephone follow-up was 86.9% (3 808/4 380) and the specific number was 0–68、0 (0, 1) times. The highest frequency of follow-up was in the first year (89.9%, 3 044/3 386) and the specific number was 0–73、5 (3, 9) times.ConclusionBy expounding the characteristics of the colorectal cancer follow-up from colorectal cancer in DACCA, it provides some references for using big data to determine prognosis.
In the context of informatization and digitization, medical big data has become crucial for promoting medical research and technological innovation, posing unprecedented challenges to the construction and operation of big data research supercomputing platforms. This article systematically elaborates on the construction plan of the scientific research supercomputing platform of the West China Biomedical Big Data Center of Sichuan University, as well as the management and service models that support data research. It also compares the scale and operation of existing scientific research supercomputing platforms at home and abroad, providing a reference for the construction and management of medical big data scientific research supercomputing platforms in other institutions.
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.
In recent years, day surgery has developed rapidly in China. Day surgery management has shifted from extensive to refined, but there are still many problems in the service system of day surgery in Chinese hospitals. In order to further optimize the allocation of medical resources, improve the level of medical service capacity, and build a “patient-centered, safe, efficient, and orderly” day surgery service system, Northern Jiangsu People’s Hospital has integrated big data, mobile internet, and artificial intelligence since 2019, creating a smart information big data platform. This paper summarizes the experience of Northern Jiangsu People’s Hospital in promoting the high-quality development of day surgery services in the whole hospital from five aspects of top-level design, diagnostic and therapeutic process, medical quality and safety, medical supporting services, and supervision mechanism, with a view to providing reference for the implementation of overall management of day surgery in the hospital.
ObjectiveTo describe the characteristics of colorectal cancer surgical procedures in the West China Colorectal Cancer Database (Database from Colorectal Cancer, DACCA).MethodWe used the form of text description.ResultsThe related content modules of DACCA operation in West China, included operative type, radical resection level, anus preservation, stoma type, the date of closure, surgical approach, expansive resection, intersphincteric resection (ISR), etc. were elaborated. The data label related method corresponding to each item in the database and the structured method required in the corresponding big data application stage were elaborated, and the error correction precautions of all classified items were described.ConclusionsIn the DACCA database, there are more detailed classification for the radical treatment of colorectal cancer. The application of expanded surgery is of great significance for both colon cancer and rectal cancer; stoma-related data has diversified data characteristics, which will provides standards and basis for clinical application of DACCA, and also provides experience reference for other colleagues who want to build colorectal cancer database.
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 September 26, 2019. The data items included: date of surgery, precancerous lesions, cancer family, tumor site, distance to the dentate line, morphology of tumor, size, position, happening and origination, differentiation, pathology of tumor, Ki-67 of protein, complications (included obstruction, intussusception, perforation, pain, edema, and hemorrhage) were analyzed for the characteristics of each selected data item.ResultsA total of 11 898 analyzable data rows were obtained by screening the DACCA database. Among the 11 898 pieces of data, the effective data of precancerous lesions was 1 275, including 541 (42.4%) with precancerous lesions, and 734 (57.6%) without precancerous lesions. There were 1 116 valid data on cancer families, and 761 (6.4%) had a family history of cancer. The Ki-67 index had a total of 1 893 valid data, which ranged form 0 to 95% [(59.0±20.1) %]. According to the classification of tumor occurrence, the primary colorectal cancer accounted for the vast majority (92.8%), and the metastatic colorectal cancer was the least (0.3%). According to the primary and multiple primary, respectively analysis of tumor site, distance to the dentate line, morphology of tumor, size, position, differentiation, and pathology of tumor showed that, most tumor’s position were in the rectum (76.9%, 41.9%), the most common morphology was ulcers (42.4%, 51.5%), the most tumors were located around the wall of intestine (44.6%, 35.0%), the degree of differentiation was mostly moderate (65.4%, 61.3%), most of the tumor pathologies were adenocarcinoma (77.8%, 64.0%).ConclusionA more accurate and detailed analysis of colorectal cancer tumor characteristics by the DACCA database is helpful for determining the diagnosis and treatment plan in clinical work, judging the prognosis, and so on.
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.