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.
Clinical trial transparency, include clinical trial registration, unbiased reporting results and sharing individual participant data (IPD), is one of the most important revolutionary concepts following clinical epidemiology and evidence-based medicine in the medical field. Sharing IPD is a medical ethics issue reflected a new sense of worth and constructing new rules of clinical trials. Our viewpoint is that from the essential purpose of clinical research, IPD is a social public property. Sharing IPD is a one of the best ways for respecting the contributions of the participants, and one of the keys for changing face of clinical trials.
In 2019, the national government issued the document "Implementation Plan for Supporting the Construction of the Boao Lecheng International Medical Tourism Pilot Area", which allowed the use of innovative drugs and medical devices in medical institution of Boao Lecheng. These medical products had been designed to meet urgent clinical requirements and had been approved by regulatory authorities overseas. Through the use of these medical products, real-world data were generated in the routine clinical practice, based on which real-world evidence might be produced for regulatory decision-making by using scientific and rigorous methods. In March 2020, the first medical device product using domestic real-world data was approved, suggesting that the real-world data initiative in Boao Lecheng achieved initial success. This work also provided important experience for promoting the practice of medical device regulatory decision-making based on real-world evidence in China. Here, we shared the preliminary experiences from the study on the first approved medical device product and discussed the issues on developing a real-world data research framework in Boao Lecheng in attempt to offer insights for future studies.
Interrupted time series (ITS) analysis is a quasi-experimental design for evaluating the effectiveness of health interventions. By controlling the time trend before the intervention, ITS is often used to estimate the level change and slope change after the intervention. However, the traditional ITS modeling strategy might indicate aggregation bias when the data was collected from different clusters. This study introduced two advanced ITS methods of handling hierarchical data to provide the methodology framework for population-level health intervention evaluation.
Hazard ratio (HR) is usually regarded as the effect size in survival studies. Meanwhile, it is supposed to be perfect for pooling results in the meta-analysis of survival data. However, it does not function usually due to absence of original data for pooling HR. As a compromise method, entering data from reading Kaplan-Meier curves and follow-up times into the calculation spreadsheet can also be used to obtain related survival data. But related study on the subject is scarce, and opinions are inconsistent. Accordingly, we conduct this study to further illustrate the procedure in details.
Breast cancer is a malignancy caused by the abnormal proliferation of breast epithelial cells, predominantly affecting female patients, and it is commonly diagnosed using histopathological images. Currently, deep learning techniques have made significant breakthroughs in medical image processing, outperforming traditional detection methods in breast cancer pathology classification tasks. This paper first reviewed the advances in applying deep learning to breast pathology images, focusing on three key areas: multi-scale feature extraction, cellular feature analysis, and classification. Next, it summarized the advantages of multimodal data fusion methods for breast pathology images. Finally, the study discussed the challenges and future prospects of deep learning in breast cancer pathology image diagnosis, providing important guidance for advancing the use of deep learning in breast diagnosis.
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 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.
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.
Cancer gene expression data have the characteristics of high dimensionalities and small samples so it is necessary to perform dimensionality reduction of the data. Traditional linear dimensionality reduction approaches can not find the nonlinear relationship between the data points. In addition, they have bad dimensionality reduction results. Therefore a multiple weights locally linear embedding (LLE) algorithm with improved distance is introduced to perform dimensionality reduction in this study. We adopted an improved distance to calculate the neighbor of each data point in this algorithm, and then we introduced multiple sets of linearly independent local weight vectors for each neighbor, and obtained the embedding results in the low-dimensional space of the high-dimensional data by minimizing the reconstruction error. Experimental result showed that the multiple weights LLE algorithm with improved distance had good dimensionality reduction functions of the cancer gene expression data.
