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 analyze the characteristics of adjuvant treatment of colorectal cancer in the Database from Colorectal Cancer (DACCA).MethodsThe informations in the DACCA database were screened, including adjuvant therapy (adjuvant strategy, compliance), adjuvant chemotherapy (indication selection, acceptance, actual cycles of chemotherapy, effect, and standardized application), adjuvant radiotherapy (indication selection, acceptance, and effect), and targeted therapy (uses of oral and intravenous targeted drugs). The data that at least one of items must not be “empty” were selected.ResultsA total of 3 955 data items were analyzed for colorectal cancer adjuvant therapy. ① The highest data composition ratio of “planned strategy of adjuvant therapy” and “compliance of adjuvant therapy” was “adjuvant therapy” (35.6%, 929/2 611) and “coordination” (28.1%, 664/2362), respectively. ② The highest data composition ratios of “indication of chemotherapy”, “acceptance of chemotherapy”, “cycles of chemotherapy”, “effect of chemotherapy”, and “chemotherapy based guidelines” were “must” (38.6%, 1 140/2 963), “rejection” (53.1%, 1 373/2 586), “6-cycle adjuvant chemotherapy” (12.4%, 338/2 722), “stability” (59.9%, 618/1031), and “standardization” (78.6%, 903/1 149). There was an obvious relationship between the planned strategy of adjuvant chemotherapy and the final acceptance of chemotherapy (χ2=505.262, P<0.001), that was, when the planned strategy of adjuvant chemotherapy was “optional”, the proportion of final rejection was very high (89.0%, 137/154). ③ The highest data composition ratios of “indication of radiation”, “acceptance of radiation”, and “effect of radiation” were “unnecessary” (49.1%, 1 423/2 915), “rejection” (93.8%, 2 629/2 803), and “stability” (38.1%, 45/118). There was a correlation between the planned strategy of adjuvant radiotherapy and the final acceptance of radiotherapy (χ2=139.593, P<0.001), that was, when the patients who should receive radiotherapy had not high acceptance (10.6%, 127/1 194), and the patients who should select optional radiotherapy all refused radiotherapy (100%).④ The data composition ratios of “none” of oral and intravenous targeted therapy drugs in targeted therapy were the highest, at 84.2% (2 121/2 520) and 73.3% (206/281), respectively. ConclusionBy expounding the characteristics of the current adjuvant treatment of colorectal cancer in DACCA, it provides a reference for the adjuvant treatment of colorectal cancer.
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 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.
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 discuss the scientific research and application value of the new China Association Against Epilepsy (CAAE) EEG reporting system, and to explore the model of establishing EEG database of tertiary comprehensive epilepsy center. MethodsA retrospective study was performed on outpatients who underwent EEG examination at the Epilepsy Center of Tsinghua University Yuquan Hospital from May 2021 to May 2022, and who also received EEG reports using the CAAE new EEG reporting system. We integrated the data of these 6380 patients with the previous database of our Epilepsy Center, and combined the two for the preliminary big data analysis. Results Among 6380 patients, normal EEG was reported in 2253 cases (35.3%) ,abnormal EEG in 4031 cases (63.2%), no definite abnormality in 96 cases. According to age groups, there were 3290 cases in children (51.0%), 1372 cases in adults (22.0%), 753 cases in adolescents (12.0%), 730 cases in infants (11.0%) and 235 cases in infants (4.0%).A total of 1466 (23.0%) patients were recorded with paroxysmal events, including 874 (60.0%) epileptic events. 517 (35.0%) non-epileptic events. ConclusionThe new EEG reporting system can provide a large number of researchable EEG data to guide clinical work, and it is an important tool for data sharing and big data research in the future.
ObjectiveTo elaborate constitute, definition, and interpretation of operative characteristics 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 operative characteristics module of colorectal cancer mainly included five parts: surgical characteristics, surgical methods, operative skills in details, surgical difficulties, and surgical quality evaluation. Then the surgical characteristics were 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 speci-fication of surgical characteristics of colorectal cancer in DACCA in West China Hospital, it can provide a reference for standar-dized 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.
As an interdisciplinary subject of medicine and artificial intelligence, intelligent diagnosis and treatment has received extensive attention in both academia and industry. Traditional Chinese medicine (TCM) is characterized by individual syndrome differentiation as well as personalized treatment with personality analysis, which makes the common law mining technology of big data and artificial intelligence appear distortion in TCM diagnosis and treatment study. This article put forward an intelligent diagnosis model of TCM, as well as its construction method. It could not only obtain personal diagnosis varying individually through active learning, but also integrate multiple machine learning models for training, so as to form a more accurate model of learning TCM. Firstly, we used big data extraction technique from different case sources to form a structured TCM database under a unified view. Then, taken a pediatric common disease pneumonia with dyspnea and cough as an example, the experimental analysis on large-scale data verified that the TCM intelligent diagnosis model based on active learning is more accurate than the pre-existing machine learning methods, which may provide a new effective machine learning model for studying TCM diagnosis and treatment.
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.