ObjectiveScreening the Database from Colorectal Cancer (DACCA) based on West China Hospial data by " Operation Date”, we purposed to analyze the population characteristics of colorectal cancer patients in regional medical center within recent Database Version.MethodsThe DACCA Version was updated in December 12th, 2018. Personal data (including sex, age, blood type, height, weight, and BMI), location data (including provinces, cities, and subordinate areas in Chengdu), occupation and education data, and main diagnosis data were included in the items. Characteristic analysis was performed on each selected data item.ResultsAccording to screening, 9 633 analytical data rows were obtained. Based on the database information, there were 24 consecutive years from 1995 to 2018 into every year. We set 2005 to 2006 as the time node for the database construction. The contribution to database before 2005 (including) was 1 358, while after 2005 (not including) were 8 275. The contribution rate (contribution numbers/years) after 2005 was higher than before 2005 ［1 358/11 vs. 8 275/13, 95% CI was (–625.337, –400.831), P<0.001］. According to gender distribution, total male data were 4 669, female were 3 340, non-checked were 1 624. According to age distribution, age were from 13 to 104 ［(59±13) years］. Linear prediction was used to predict the age distribution with the " year” as the time axis. The results showed the stable linear prediction (\begin{document}$\hat y$\end{document}=0.016 1x+26.54, R2=3.42×105, P=0.601 108). According to height, height were from 138 cm to 192 cm ［(161±7)cm］, linear prediction results showed that the linear variation with height changes by value (\begin{document}$\hat y$\end{document}=0.110 5 x–60.911, R2=0.002 6, P=0.000 272). According to weight, weight were from 27.5 kg to 80.5 kg ［(59.38±10.27) kg］, linear prediction results showed that the linear variation with height changes by value (\begin{document}$\hat y$\end{document}=0.296 5x–537.24, R2=0.010 625, P=2.37×1014). Available 6 884 data showed the difference between serving areas by West China Hospital and official definition of western region. A total of 9 209 data obtained by analyzing main diagnosis, showed that the main site of disease was rectum (68.64%). Sigmoid was the main location of colon cancer (68.64%), and anal-rectal cancer was main of rectal cancer (27.06%).ConclusionPopulation characteristics from DACCA database could initially reflect the trend of increasing weight and BMI of colorectal cancer patients, and also reflect the regional distribution characteristics based on geographic information. They would be the clues for further database research.
ObjectiveBased on recently update Database from Colorectal Cancer (DACCA), we aimed to analyze the characteristics of in-hospital process management from reginal medical center’s colorectal cancer patients.MethodsWe used Version January 29th, 2019 of DACAA being the analyzing source. The items were included date of first out-patient meeting, admitted date, operative date, discharged date, waiting-time, preoperative staying days, postoperative staying days, hospital staying days, and manage protocol, whose characteristics would be analyzed.ResultsWe left 8 913 lines to be analyzed by filtering DACCA. Useful data lines of first out-patient meeting had 3 915, admitted date had 8 144, operative date had 8 049, and discharged date had 7 958. The average of waiting-time were (9.41±0.43) days, and based on timeline trend for line prediction analyzing, which showed R2=0.101 257, P<0.001. The average of preoperative staying days were (5.41±0.04) days, and based on timeline trend for line prediction analyzing, which showed R2=0.023 671, P<0.001. The average of postoperative staying days were (8.99±0.07) days, and based on timeline trend for line prediction analyzing, which showed R2=0.086 177, P<0.001. The average of hospital staying days were (14.43±0.08) days, and based one timeline trend of line prediction analyzing, which showed R2=0.098 44, P<0.001. Analyzable ERAS data were 2 368 lines in DACCA. Total EARS data in 2 368 lines, there were 108 lines (5%) completed and 2 260 lines (95%) incomplete. Pre/post ERAS data in 2 260 lines, there were 150 lines (7%) completed and 2 110 lines (93%) incomplete. Post ERAS data in 2 110 lines, there were 170 lines (8%) completed and 1 940 lines (92%) incomplete.ConclusionsIn recent 20 years, the regional medical center served in-hospital colorectal cancer patients with decreased preoperative staying days, postoperative staying days, and in-hospital staying days from DACCA analyzing, which could prove the service ability had been in improved. Utilization rate of EARS was increased, and also being the main in-hospital process management.
ObjectiveTo explain in detail hospitalization process management of colorectal cancer as well as its tag and structure of Database from Colorectal Cancer (DACCA) in the West China Hospital.MethodThe article was described in the words.ResultsThe definition and setting of 8 classification items involved in the hospitalization process management from DACCA in the West China Hospital were set. The items were included the date of first out-patient meeting, admitted date, operative date, discharged date, waiting time before the admission, preoperative staying days, total hospital staying days, and manage protocol. The relevant data tag of each item and the structured way needed at the big data application stage were elaborated and the corrective precautions of classification items were described.ConclusionsBased on description about hospitalization process management from DACCA in West China Hospital, it is provided a clinical standard and guidance for analyzing of DACCA in West China Hospital in future. It also could provide enough experiences for construction of colorectal cancer database by staff from same occupation.
ObjectiveBased on the current version of Database from Colorectal Cancer (DACCA), we aimed to analyze the comorbidities and preoperative physical status of colorectal cancer patients.MethodsThe DACCA version selected for this data analysis was updated on May 9, 2019. The data items included: surgical comorbidities and classified by systems, surgical history, pelvic disease history, medical comorbidities, and some important subdivision types, infectious disease status, allergic history, nutrition risk screening 2002 (NRS2002) score, amount of weight loss after illness, anemia, low protein status, preoperative ascites status, preoperative pleural effusion status, immune system disease and immunocompromised status, and preoperative nutritional support. Characteristic analysis was performed on each selected data item.ResultsA total of 6 166 admitted data were filtered from the DACCA database. Among them, surgical comorbidities, surgical history, medical comorbidities, and allergy history had 6 166 admitted data, and weight loss had admitted 4 703. There were 2 923 (47.4%) with surgical comorbidities. According to the system, the most common one was digestive system (2 005, 68.6%), and the least one was skin tissue system (24, 0.8%). There were 4 361 (70.7%) patients without surgical history and 1 805 (29.3%) patients had surgical history. There were 2 397 (38.9%) patients without medical comorbidities and 3 769 (61.1%) had medical comorbidities, of which pneumonia/pulmonary infection/chronic bronchopneumonia/lung indeterminate nodules were the most common(2 330, 37.8%), the least was cerebral infarction (unspecified type, 63, 1.0%). There were 5 813 (94.3%) without allergy history and 353 (5.7%) had allergy history. According to the NRS2002 nutrition screening criteria, the scores ranged from 1 to 7 points, with an average of 1.22 points, which could be classified as non-nutrition risk (5 279, 85.6%, included 1 point of 4 310, 2 points of 969), nutritional risk (887, 14.4%, included 3 points of 415, 4 points of 358, 5 points of 100, 6 points of 12, and 7 points of 2), the result of linear regression analysis of NRS2002 scores with the trend of the year showed that: ŷ=0.000 2x–6.275 8, R2=0.716 2, P<0.001. A total of 2 840 (60.4%) had no weight loss while 1 863 (39.6%) had, and weight loss with the trend of year were analyzed by linear regression analysis: ŷ=0.000 2x–3.956, R2=0.685 7, P<0.001. The number of cases of other physical status and the proportion of valid data were anemia (1 194, 33.1%), preoperative ascites (1829, 51.7%), preoperative pleural effusion (171, 5.7%), hypoproteinemia (1 206, 33.6%), immune system disease and immunocompromised status (495, 56.6%), and nutritional support (824, 25.0%).ConclusionsThrough the analysis of the DACCA database, nearly 1/2 of colorectal cancer surgery patients have surgical comorbidities before surgery, more than 1/2 of the patients have medical comorbidities, and the types of diseases are various. Preoperative nutritional status in patients with colorectal cancer also shows certain characteristics, suggesting the state of preoperative risk. These data will provide a detailed big data basis for future preoperative risk assessment of colorectal cancer.
ObjectiveBased on the current version of Database from Colorectal Cancer (DACCA), we aimed to analyze the preoperative specialized examination and evaluation of colorectal cancer.MethodsThe DACCA version selected for this data analysis was updated on July 25, 2019. The data items included: combined preoperative stage, integrating degree of combined preoperative stage, preoperative diagnostic intensity, accuracy of colonoscopy, tumorous type by biopsy, tumor differentiation by biopsy, completion of chest CT, CT stage, accuracy of CT stage, outcome of transrectal ultrasound, outcome of liver ultrasound, MRI stage, accuracy of MRI stage, outcome of PET-CT, outcome of bone scanning, diagnostic way at first visit, misdiagnosis and mistreatment. Characteristic analysis was performed on each selected data item.ResultsA total of 4 484 admitted data were filtered from the DACCA database. The effective data of accuracy of preoperative CT examination, evaluation of preoperative CT staging, preoperative MRI accuracy, preoperative MRI evaluation stage, the accuracy of preoperative transrectal ultrasound, preoperative liver ultrasound accuracy, the accuracy of preoperative bone scan, preoperative PET-CT accuracy, completion of colonoscopy, preoperative colonoscopy biopsy pathology type, strength of diagnosis, integrating degree of total preoperative staging, preoperative staging and pathological staging, factors of the first diagnosis, misdiagnosis and mistreatment were 3 877 (86.5%), 3 166 (70.6%), 3 480 (77.6%), 286 (6.4%), 3 607 (80.4%), 2 736 (61.0%), 3 570 (79.6%), 3 490 (77.8%), 3 847 (85.8%), 3 636 (81.1%), 3 981 (88.8%), 2 346 (52.3%), 2 209 (49.3%), 3 466 (77.3%), and 3 411 (76.1%), respectively. Among the preoperative CT stages, phase Ⅳ had the highest accuracy (86.6%), phase Ⅰ had the highest rate of underestimation (30.4%), and phase Ⅲ had the highest rate of overestimation (21.8%). Preoperative CT accuracy, excluding errors caused by too few data rows, was 66.8%–83.7% in other years. Among the preoperative MRI stages, stage Ⅳ showed the highest accuracy (89.1%), stage Ⅰ showed the highest rate of underestimation (33.3%), and stage Ⅲ showed the highest rate of overestimation (13.3%). Preoperative MRI evaluation accuracy gradually increased from 2016 to 2019. The accuracy of transrectal ultrasound, liver ultrasound, bone scan, and PET-CT were 287 (76.7%), 145 (99.3%), 301 (98.7%), and 15 (93.8%), respectively. The most pathological type under colonoscopy was adenocarcinoma, accounting for 82.2%. The lowest was stromal tumor and lymphoma, each below 0.1%. The diagnostic efficiency were 3 445 (86.5%) with grade A, 316 (7.9%) with grade B, and 220 (5.5%) with grade C. In the preoperative total staging, 109 data rows (4.9%) were appeared as stage Ⅰ, 615 (27.5%) as stage Ⅱ, 1 263 (56.6%) as stage Ⅲ, and 245 (11.0%) as stage Ⅳ. The preoperative total staging integrating degree in stage Ⅳ was the highest (98.7%), while the underestimate rate in stage Ⅱ was the highest (28.3%), and the overestimate rate in stage Ⅲ was the highest (20.6%). From 2008 to 2019, the integrating degree between preoperative comprehensive staging and final pathology staging ranged from 70.8% to 87.7%. Among the factors of the first diagnosis, digital examination was found the frequently (64.0%), followed by symptoms such as bleeding and obstruction (28.2%). Considering family history, the proportion of patients with colorectal cancer was the least (less than 0.1%). There were 442 cases (13.0%) of misdiagnosis and mistreatment behaviors, among which 207 cases (46.8%) were misdiagnosed as hemorrhoids.ConclusionsTo significantly improve the long-term survival rate of colorectal cancer patients requires preoperative imaging diagnosis efficiency and multi-factor evaluation staging to break through the limitation of development, so as to optimize the choice of treatment plan, increasing the prevalence of early screening for colorectal cancer, and reducing the rate of misdiagnosis and mistreatment at the first visit of colorectal cancer.
ObjectiveTo elaborate the contents and concrete concepts of preoperative specialized examination and evaluation of colorectal cancer of the Database from Colorectal Cancer (DACCA) in the West China Hospital. MethodThe article was described in the words.ResultsThe components, stage, accuracy, preoperative comprehensive evaluation, clinical factors of initial diagnosis, misdiagnosis and mistreatment of colorectal cancer in the DACCA were defined and elaborated in the detail. The data label corresponding to each item in the database and the required structured way in the application stage of large data were also described in detail, and the corrective precautions for all classified items were described.ConclusionsThrough the detailed description of the preoperative specialized examination and evaluation of colorectal cancer of DACCA in West China Hospital, it might provide the standard and basis for the clinical application of database in the future, and provide reference for other peers who wish to build a colorectal cancer database.
ObjectiveTo analyze the geographical distribution of patients with colorectal cancer by screening the current Database from Colorectal Cancer (DACCA) version in West China Hospital.MethodsThe selected DACCA database version of this data analysis was updated on September 5, 2019, and the two data of the " date of operation” and " address” were selected as the main research items. The characteristics of each selected data item were analyzed, and then the selected data were used as a joint feature analysis.ResultsAccording to the condition of selection by " address”, 7 096 valid data rows from the whole nation were obtained, 6 551 valid data rows from Sichuan province were obtained, and 2 954 valid data rows from Chengdu city were obtained. The geographic information provided by the DACCA database showed that, with the year changing, the provincial distribution area of patients was mainly the southwest region with middle-east of Sichuan province as center, mainly including the parts of Chongqing, Yunnan, and Guizhou; The distribution area of the municipal level in Sichuan province was mainly the east region with axis of the " Mianyang-Chengdu-Ya’an”, and Chengdu was the core; The regional distribution of patients in the Chengdu was mainly within the third ring load with Wuhou District, the Jinniu District, and the Qingyang District as the core area.ConclusionThegeographical information provided by DACCA database shows the geographical distribution characteristics of patients in the past 20 years, reflecting the basic characteristics and changes of the service area of West China Hospital, and can provide a basis for medical policy makers in screening, diagnosing and treating of colorectal cancer, and key management areas of following-up.
ObjectiveTo elaborate constitute, definition, and interpretation of tumor characteristics module of colorectal cancer in the Database from Colorectal Cancer (DACCA) in the West China Hospital.MethodThe article was described in the words.ResultsThe tumor features module of colorectal cancer in the DACCA included the precancerous lesion, cancer family, location of tumor, distance to the dentate line, morphology of tumor, size, position, happening and origination, differentiation, pathology of tumor, Ki-67 protein, obstruction, intussusception, perforation, pain, edema, and hemorrhage. The exact definitions of morphology of tumor, size, position, differentiation, pathology of tumor, Ki-67 protein and complication (included obstruction, intussusception, perforation, pain, edema, and hemorrhage), tag and structure, corrective precautions and update of these columns, and how to use these tumor characteristics in the DACCA when analysis was carried out were described in detail.ConclusionThrough detailed description and specification of current tumor characteristics 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.
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.
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.