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.
Objective To establish a cooperative decision-making model of county-level public hospitals, so as to freely select the best partner in different decision-making units and promote the optimal allocation of medical resources. Methods The input and output data of 10 adjacent county-level public hospitals in Henan province from 2017 to 2019 was selected. Based on the traditional data envelopment analysis (DEA) model, a generalized fuzzy DEA cooperative decision-making model with better applicability to fuzzy indicators and optional decision-making units was constructed. By inputting index information such as total number of employees, number of beds, annual outpatient and emergency volume, number of discharged patients, total income and hospital grade evaluation, the cooperation efficiency intervals of different hospitals were calculated to scientifically select the best partner in different decision-making units.Results After substituting the data of 10 county-level public hospitals in H1-H10 into the model, taking H2 hospital as an example to make cooperative decision, among the four hospitals in H1, H2, H7 and H10 of the same scale, under optimistic circumstances, the best partner of H2 hospital was H7 hospital, and the cooperation efficiency value was 1.97; in a pessimistic situation, the best partner of H2 hospital was H10 hospital, and the cooperation efficiency value was 0.98. The model had good applicability in the cooperative decision-making of county-level public hospitals. Conclusion The generalized fuzzy DEA model can better evaluate the cooperative decision-making analysis between county-level public hospitals.
Considering the low accuracy of prediction in the positive samples and poor overall classification effects caused by unbalanced sample data of MicroRNA (miRNA) target, we proposes a support vector machine (SVM)-integration of under-sampling and weight (IUSM) algorithm in this paper, an under-sampling based on the ensemble learning algorithm. The algorithm adopts SVM as learning algorithm and AdaBoost as integration framework, and embeds clustering-based under-sampling into the iterative process, aiming at reducing the degree of unbalanced distribution of positive and negative samples. Meanwhile, in the process of adaptive weight adjustment of the samples, the SVM-IUSM algorithm eliminates the abnormal ones in negative samples with robust sample weights smoothing mechanism so as to avoid over-learning. Finally, the prediction of miRNA target integrated classifier is achieved with the combination of multiple weak classifiers through the voting mechanism. The experiment revealed that the SVM-IUSW, compared with other algorithms on unbalanced dataset collection, could not only improve the accuracy of positive targets and the overall effect of classification, but also enhance the generalization ability of miRNA target classifier.
Objective To collect the data of nosocomial infection surveillance in secondary and tertiary medical institutions in Jiangsu Province and conduct comparative analysis among different regions to find out the gaps and provide basis for targeted continuous improvement. MethodsCombined with the requirements of grade hospital evaluation and high-quality development of public hospitals, the data of nosocomial infection incidence in medical institutions of Jiangsu Province in the first quarter of 2023 were collected by autonomous reporting and information capture, and the province was divided into three regions according to location: South Jiangsu, Central Jiangsu and North Jiangsu for analysis, so as to evaluate the regional level. Results A total of 109 medical institutions were included, including 78 tertiary hospitals and 31 secondary hospitals. The overall incidence rate of nosocomial infection was 0.81% (0.90%). The incidence of central line-associated bloodstream infection (CLABSI), ventilator-associated pneumonia (VAP) and catheter-associated urinarytract infection (CAUTI) were 0.113‰, 1.553‰ and 0.424‰, respectively. The proportion of prophylactic drugs in Class Ⅰ incision and the incidence of surgical site infection in Class Ⅰ incision were 17.72% and 0.16%, respectively. In the above infection indicators, the incidences of CLABSI and VAP were higher in Central Jiangsu, while the other indicators were higher in South Jiangsu than in Central Jiangsu than in North Jiangsu. The utilization rate of antibiotics and the detection rate of pathogens in inpatients were 41.07% and 41.50%, respectively. Among South, North, and Central Jiangsu, the utilization rate of antibiotics was 41.83%, 41.51%, and 39.51%, respectively (χ2=446.789, P<0.001), and the detection rate of pathogens was 46.09%, 40.94%, and 35.09%, respectively (χ2=3036.865, P<0.001). In the detection rate of drug-resistant bacteria infection, the top 3 were carbapenem-resistant Acinetobacter baumannii (0.067%), carbapenem-resistant Klebsiella pneumoniae (0.031%) and methicillin-resistant Staphylococcus aureus (0.029%). Among them, the infection rate of drug-resistant bacteria in South Jiangsu was significantly higher than that in the other two regions. The detection rates of carbapenem-resistant Acinetobacter baumannii and methicillin-resistant Staphylococcus aureus were close to each other (83.77% vs. 53.91%), while the detection rates of carbapenem-resistant Escherichia coli (χ2=95.619, P<0.001), carbapenem-resistant Klebsiella pneumoniae (χ2=520.855, P<0.001) and carbapenem-resistant Pseudomonas aeruginosa (χ2=191.918, P<0.001) in South Jiangsu were significantly higher than those in Central and North Jiangsu. Conclusions There are significant differences in nosocomial infection surveillance data of medical institutions in different regions of Jiangsu Province. It is emphasized that targeted quality control feedback, supervision and rectification should be carried out while hospital infection monitoring.
Network meta-analyses (NMA) of survival data often rely on the proportional hazards (PH) assumption, however, this assumption fails when survival curves intersect. With the emergence of innovative therapies such as immunotherapy, the importance of NMA based on non-proportional hazards (non-PH) in the current evidence-based medicine evaluation of oncology drugs has become increasingly prominent. Fractional polynomial (FP) models do not rely on the assumption of PH, which can flexibly capture the characteristics of survival curves, and the corresponding fitting effects are better than those of the PH models. This study introduced a complete workflow in R for NMA using FP models with non-PH.
This is the seventh paper in the evidence-based medicine glossary series. In this paper, We mainlyintroduced five terms related to meta-analysis——prospective meta-analysis, individual patient data meta-analysis,cumulative meta-analysis, multiple-treatments meta-analysis and meta regression.We also gave some examples to helpreaders better understand and use them.
The minimally invasive cardiovascular surgery developed rapidly in last decades. In order to promote the development of minimally invasive cardiovascular surgery in China, the Chinese Minimally Invasive Cardiovascular Surgery Committee (CMICS) has gradually standardized the collection and report of the data of Chinese minimally invasive cardiovascular surgery since its establishment. The total operation volume of minimally invasive cardiovascular surgery in China has achieved substantial growth with a remarkable popularization of concepts of minimally invasive medicine in 2019. The data of Chinese minimally invasive cardiovascular surgery in 2019 was reported as a paper for the first time, which may provide reference to cardiovascular surgeons and related professionals.
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.
Objective To analyze the impact of body mass index (BMI) on tumor characteristics of colorectal patients served by West China Hospital as a regional center in the current version of Database from Colorectal Cancer (DACCA). MethodsThe data of DACCA was updated on October 16, 2021. All data items included BMI, precancerous lesions, cancer family, tumor site, tumor morphology, location, differentiation, pathological properties of tumor, obstruction, overlap, perforation, pain, edema, and bleeding. The patients were divided into lean (BMI<18.5 kg/m2), normal (BMI 18.5–23.9 kg/m2), overweight (BMI 24.0–27.9 kg/m2) and obesity (BMI≥28.0 kg/m2) by Chinese classification methods. ResultsAfter scanning, 5 761 data rows were analyzed. Chi-square test showed that there was significant difference in the type composition ratio of tumor location in colorectal cancer patients under different BMI groups (χ2=31.477, P<0.001). Rank sum test showed that there was significant difference in the degree of obstruction (H=42.490, P<0.001), intussusception (H=8.179, P=0.042), edema (H=14.795, P=0.002), and bleeding (H=9.884, P=0.020) among different BMI groups. ConclusionsThe BMI classification of colorectal cancer patients is related to the location of tumor and the occurrence of some tumor complications. Patients with tumor involving intestinal lumens for one week are more likely to have low BMI. The patients with low BMI are more likely to have severe bleeding, obstruction, intestinal intussusception, and severe intestinal wall edema.
Rare diseases are characterized by low incidence rates, high heterogeneity, and significant genetic relevance, posing global challenges in clinical diagnosis and treatment, including delayed diagnosis and a scarcity of therapeutic options. Artificial intelligence (AI) technology offers novel solutions to address these challenges in the field of rare diseases. This paper explores the advancements in AI applications for rare diseases from two perspectives: auxiliary diagnosis and treatment decision-making. In terms of auxiliary diagnosis, AI can integrate superficial features, electronic health records, genomic data, and multi-modal data to achieve early and precise diagnosis. Regarding treatment decision-making, AI facilitates drug target discovery, drug repurposing, and the design of gene therapy vectors, thereby promoting the development and application of new treatments. Furthermore, this paper analyzes the challenges of AI in rare disease diagnosis and treatment concerning data, technical algorithms, and clinical application, and proposes future directions, including the construction of a collaborative data ecosystem, enhancement of algorithm interpretability, and improvement of regulatory frameworks.