ObjectiveTo evaluate the operation status of the clinical departments of a hospital through the establishment of the evaluation index system and comprehensive evaluation model.MethodsThe data on workload, service difficulty, service efficiency, health economics and other related indicators of the clinical departments of a hospital from January to June 2018 were collected. The comprehensive evaluation model was constructed by comprehensive scoring method. The data of each index were centralized, then the comprehensive evaluation model of clinical departments was established and the scores were calculated by weighted summation. Microsoft Excel 2010 and SPSS 17.0 software were used for data processing.ResultsThere were certain differences in comprehensive scores and detailed indicators among different clinical departments. Ranked by comprehensive scores, the top three surgical departments were Department of Thoracic Surgery (1.45), Department of Breast Surgery (1.32), and Department of Vascular Surgery (1.22), and the top three internal departments were Department of Oncology (5.76), Department of Cardiology (3.47), and Department of Hematology (3.41).ConclusionsIn general, there are some differences in the operating conditions among different departments. There are also differences in the detailed indicators among different departments. The results can be used to find out problems and gaps, and finally improve the operation of the departments.
ObjectiveBased on a large sample of data, study the factors affecting the survival and prognosis of patients with rectal cancer and construct a prediction model for the survival and prognosis.MethodsThe clinical data of 26 028 patients with rectal cancer were screened from the Surveillance, Epidemiology, and End Results (SEER) clinical database of the National Cancer Institute. Univariate and multivariate Cox proportional hazard regression analysis were used to screen related risk factors. Finally, the Nomogram prediction model was summarized and its accuracy was verified.ResultsResult of multivariate Cox proportional hazard regression analysis showed that the risk factors affecting the survival probability of rectal cancer included: age, gender, marital status, TMN staging, T staging, tumor size, degree of tissue differentiation, total number of lymph nodes removed, positive lymph node ratio, radiotherapy, and chemotherapy (P<0.05). Then we further built the Nomogram prediction model. The C index of the training cohort and the validation cohort were 0.764 and 0.770, respectively. The area under the ROC curve (0.777 and 0.762) for 3 years and 5 years, and the calibration curves of internal and external validation all indicated that the model could effectively predict the survival probability of rectal cancer.ConclusionThe constructed Nomogram model can predict the survival probability of rectal cancer, and has clinical guiding significance for the prognostic intervention of rectal cancer.
Judging from the latest policies related to the medical insurance payment reform of the state and Sichuan province, the reform of medical insurance diagnosis-related group (DRG)/diagnosis-intervention packet (DIP) payment methods is imperative. The impact of DRG/DIP payment method reform on public hospitals is mainly analyzed from the aspects of hospital cost accounting and control, quality of filling in the first page of medical cases, coding accuracy, standard of medical practice, development of diagnosis and treatment technology innovation business, multi-departmental linkage mechanism, competition between hospitals, performance appraisal mechanism, and negotiation and communication mechanism. We should put forward hospital improvement strategies from the top-level design of the whole hospital and from the aspects of improving the quality of the first page of the cases and the quality of the coding, strengthening the cost accounting and control of the disease, carrying out in-hospital and out-of-hospital training, establishing a liaison model, finding gaps with benchmark hospitals, enhancing the core competitiveness of innovative technologies, and improving internal performance appraisal, etc., to promote the high-quality development of hospitals.
Tuberculosis is a chronic infectious diseases caused by Mycobacterium tuberculosis. Its high morbidity and mortality have posed a serious threat to global public health. Matrix metalloproteinase (MMP) is a proteolytic enzyme involved in regulating extracellular matrix degradation and remodeling. MMP is highly expressed in pulmonary tuberculosis, and its expression is regulated by genes, epigenetic modifications, cellular signaling pathways, immune regulation, and cellular environment. MMP is a potential target for the treatment of pulmonary tuberculosis. Therefore, this article summarizes the expression and related mechanisms of MMP in pulmonary tuberculosis, aiming to provide a reference for the diagnosis and treatment of pulmonary tuberculosis.
ObjectiveTo systematically review the relationship between systemic immune inflammatory index (SII) and the prognosis of coronary heart disease. MethodsThe CNKI, VIP, CBM, WanFang Data, PubMed, EMbase, Web of Science, Ovid, Cochrane Library and Scopus databases were electronically searched to collect cohort studies related to the relationship between SII and the prognosis of patients with coronary heart disease from inception to December 10, 2022. Two researchers independently screened the literature, extracted the data and assessed the risk of bias of the included studies. Meta-analysis was then performed by using RevMan 5.3 and Stata 15.0 software. ResultsA total of 7 cohort studies involving 18 413 patients were included. The results of meta-analysis showed that the group of high level SII was higher risk of major adverse cardiovascular events (MACE) (OR=2.2, 95%Cl 1.5 to 3.3, P<0.01), all-cause death (OR=2.0, 95%Cl 1.1 to 3.4, P=0.02), and cardiogenic death (OR=2.4, 95%Cl 1.5 to 3.9, P<0.01) than the group of low level SII. However, no significant difference was found in the risk of re-hospitalization for heart failure. ConclusionThe current evidence shows that high levels of SII can increase the risk of MACE, all-cause death and cardiogenic death in patients with coronary heart disease. Due to the limited quality and quantity of the included studies, more high quality studies are needed to verify the above conclusion.