Objective Analyzes the grouping effect and its influencing factors under DRG payment,provides reference for the reform of DRG payment.Methods Evaluates the effectiveness of DRG grouping using Coefficient of Variation(CV)and Reduction in Variance;using Value of Structure of Variation and Degree of Structure Variation,analyzes hospitalization costs structure changes of different DRG groups,and calculates the degree of correlation between average hospitalization costs through grey relational analysis;using non parametric tests and multiple regression to analyze the influencing factors of hospitalization cost.Results DRG grouping effect was not good,inter-group heterogeneity was not obvious;the structure of hospitalization expenses is unreasonable,and the proportion of consumables expenses is too high,ranking first in the grey correlation degree of hospitalization expenses,comprehensive medical service fees and treatment fees rank third and fifth respectively;the main factors affecting hospitalization costs are treatment methods,length of stay,presence of complications,and first hospitalization,the difference is statistically significant(P＜0.05).Conclusion More grouping nodes or higher CV value standards should be added to enhance the grouping effect of gastric cancer DRG;optimize the structure of hospitalization costs to reflect the labor and technical value of medical personnel;strengthen internal management and control the unreasonable use of drugs and consumables.

Objective Analyzes the grouping effect and its influencing factors under DRG payment,provides reference for the reform of DRG payment.Methods Evaluates the effectiveness of DRG grouping using Coefficient of Variation(CV)and Reduction in Variance;using Value of Structure of Variation and Degree of Structure Variation,analyzes hospitalization costs structure changes of different DRG groups,and calculates the degree of correlation between average hospitalization costs through grey relational analysis;using non parametric tests and multiple regression to analyze the influencing factors of hospitalization cost.Results DRG grouping effect was not good,inter-group heterogeneity was not obvious;the structure of hospitalization expenses is unreasonable,and the proportion of consumables expenses is too high,ranking first in the grey correlation degree of hospitalization expenses,comprehensive medical service fees and treatment fees rank third and fifth respectively;the main factors affecting hospitalization costs are treatment methods,length of stay,presence of complications,and first hospitalization,the difference is statistically significant(P＜0.05).Conclusion More grouping nodes or higher CV value standards should be added to enhance the grouping effect of gastric cancer DRG;optimize the structure of hospitalization costs to reflect the labor and technical value of medical personnel;strengthen internal management and control the unreasonable use of drugs and consumables.

Objective Analyzes the grouping effect and its influencing factors under DRG payment,provides reference for the reform of DRG payment.Methods Evaluates the effectiveness of DRG grouping using Coefficient of Variation(CV)and Reduction in Variance;using Value of Structure of Variation and Degree of Structure Variation,analyzes hospitalization costs structure changes of different DRG groups,and calculates the degree of correlation between average hospitalization costs through grey relational analysis;using non parametric tests and multiple regression to analyze the influencing factors of hospitalization cost.Results DRG grouping effect was not good,inter-group heterogeneity was not obvious;the structure of hospitalization expenses is unreasonable,and the proportion of consumables expenses is too high,ranking first in the grey correlation degree of hospitalization expenses,comprehensive medical service fees and treatment fees rank third and fifth respectively;the main factors affecting hospitalization costs are treatment methods,length of stay,presence of complications,and first hospitalization,the difference is statistically significant(P＜0.05).Conclusion More grouping nodes or higher CV value standards should be added to enhance the grouping effect of gastric cancer DRG;optimize the structure of hospitalization costs to reflect the labor and technical value of medical personnel;strengthen internal management and control the unreasonable use of drugs and consumables.

Objective Analyzes the grouping effect and its influencing factors under DRG payment,provides reference for the reform of DRG payment.Methods Evaluates the effectiveness of DRG grouping using Coefficient of Variation(CV)and Reduction in Variance;using Value of Structure of Variation and Degree of Structure Variation,analyzes hospitalization costs structure changes of different DRG groups,and calculates the degree of correlation between average hospitalization costs through grey relational analysis;using non parametric tests and multiple regression to analyze the influencing factors of hospitalization cost.Results DRG grouping effect was not good,inter-group heterogeneity was not obvious;the structure of hospitalization expenses is unreasonable,and the proportion of consumables expenses is too high,ranking first in the grey correlation degree of hospitalization expenses,comprehensive medical service fees and treatment fees rank third and fifth respectively;the main factors affecting hospitalization costs are treatment methods,length of stay,presence of complications,and first hospitalization,the difference is statistically significant(P＜0.05).Conclusion More grouping nodes or higher CV value standards should be added to enhance the grouping effect of gastric cancer DRG;optimize the structure of hospitalization costs to reflect the labor and technical value of medical personnel;strengthen internal management and control the unreasonable use of drugs and consumables.

Objective Analyzes the grouping effect and its influencing factors under DRG payment,provides reference for the reform of DRG payment.Methods Evaluates the effectiveness of DRG grouping using Coefficient of Variation(CV)and Reduction in Variance;using Value of Structure of Variation and Degree of Structure Variation,analyzes hospitalization costs structure changes of different DRG groups,and calculates the degree of correlation between average hospitalization costs through grey relational analysis;using non parametric tests and multiple regression to analyze the influencing factors of hospitalization cost.Results DRG grouping effect was not good,inter-group heterogeneity was not obvious;the structure of hospitalization expenses is unreasonable,and the proportion of consumables expenses is too high,ranking first in the grey correlation degree of hospitalization expenses,comprehensive medical service fees and treatment fees rank third and fifth respectively;the main factors affecting hospitalization costs are treatment methods,length of stay,presence of complications,and first hospitalization,the difference is statistically significant(P＜0.05).Conclusion More grouping nodes or higher CV value standards should be added to enhance the grouping effect of gastric cancer DRG;optimize the structure of hospitalization costs to reflect the labor and technical value of medical personnel;strengthen internal management and control the unreasonable use of drugs and consumables.

Objective Analyzes the grouping effect and its influencing factors under DRG payment,provides reference for the reform of DRG payment.Methods Evaluates the effectiveness of DRG grouping using Coefficient of Variation(CV)and Reduction in Variance;using Value of Structure of Variation and Degree of Structure Variation,analyzes hospitalization costs structure changes of different DRG groups,and calculates the degree of correlation between average hospitalization costs through grey relational analysis;using non parametric tests and multiple regression to analyze the influencing factors of hospitalization cost.Results DRG grouping effect was not good,inter-group heterogeneity was not obvious;the structure of hospitalization expenses is unreasonable,and the proportion of consumables expenses is too high,ranking first in the grey correlation degree of hospitalization expenses,comprehensive medical service fees and treatment fees rank third and fifth respectively;the main factors affecting hospitalization costs are treatment methods,length of stay,presence of complications,and first hospitalization,the difference is statistically significant(P＜0.05).Conclusion More grouping nodes or higher CV value standards should be added to enhance the grouping effect of gastric cancer DRG;optimize the structure of hospitalization costs to reflect the labor and technical value of medical personnel;strengthen internal management and control the unreasonable use of drugs and consumables.

Objective Analyzes the grouping effect and its influencing factors under DRG payment,provides reference for the reform of DRG payment.Methods Evaluates the effectiveness of DRG grouping using Coefficient of Variation(CV)and Reduction in Variance;using Value of Structure of Variation and Degree of Structure Variation,analyzes hospitalization costs structure changes of different DRG groups,and calculates the degree of correlation between average hospitalization costs through grey relational analysis;using non parametric tests and multiple regression to analyze the influencing factors of hospitalization cost.Results DRG grouping effect was not good,inter-group heterogeneity was not obvious;the structure of hospitalization expenses is unreasonable,and the proportion of consumables expenses is too high,ranking first in the grey correlation degree of hospitalization expenses,comprehensive medical service fees and treatment fees rank third and fifth respectively;the main factors affecting hospitalization costs are treatment methods,length of stay,presence of complications,and first hospitalization,the difference is statistically significant(P＜0.05).Conclusion More grouping nodes or higher CV value standards should be added to enhance the grouping effect of gastric cancer DRG;optimize the structure of hospitalization costs to reflect the labor and technical value of medical personnel;strengthen internal management and control the unreasonable use of drugs and consumables.

Objective Analyzes the grouping effect and its influencing factors under DRG payment,provides reference for the reform of DRG payment.Methods Evaluates the effectiveness of DRG grouping using Coefficient of Variation(CV)and Reduction in Variance;using Value of Structure of Variation and Degree of Structure Variation,analyzes hospitalization costs structure changes of different DRG groups,and calculates the degree of correlation between average hospitalization costs through grey relational analysis;using non parametric tests and multiple regression to analyze the influencing factors of hospitalization cost.Results DRG grouping effect was not good,inter-group heterogeneity was not obvious;the structure of hospitalization expenses is unreasonable,and the proportion of consumables expenses is too high,ranking first in the grey correlation degree of hospitalization expenses,comprehensive medical service fees and treatment fees rank third and fifth respectively;the main factors affecting hospitalization costs are treatment methods,length of stay,presence of complications,and first hospitalization,the difference is statistically significant(P＜0.05).Conclusion More grouping nodes or higher CV value standards should be added to enhance the grouping effect of gastric cancer DRG;optimize the structure of hospitalization costs to reflect the labor and technical value of medical personnel;strengthen internal management and control the unreasonable use of drugs and consumables.

Objective:To explore the long-term effects of intravascular ultrasound (IVUS) guidance on patients with acute coronary syndrome (ACS) undergoing drug-eluting stents (DES) implantation.Methods:Data used in this study derived from ULTIMATE trial, which was a prospective, multicenter, randomized study. A total of 1 448 all-comer patients were enrolled between 2014 August and 2017 May. Primary endpoint of this study was target vessel failure (TVF) at 3 years, including cardiac death, target-vessel-related myocardial infarction, and clinically-driven target vessel revascularization.Results:ACS was present in 1 136 (78.5%) patients, and 3-year clinical follow-up was available in 1 423 patients (98.3%). TVF in the ACS group was 9.6% (109/1 136), which was significantly higher than 4.5% (14/312) in the non-ACS group (log-rank P=0.005). There were 109 TVFs in the ACS patients, with 7.6% (43/569) TVFs in the IVUS group and 11.6% (66/567) TVFs in the angiography group (log-rank P=0.019). Moreover, patients with optimal IVUS guidance were associated with a lower risk of 3-year TVF compared to those with suboptimal IVUS results (5.4% (16/296) vs. 9.9% (27/273),log-rank P=0.041). Conclusions:This ULTIMATE-ACS subgroup analysis showed that ACS patients undergoing DES implantation were associated with a higher risk of 3-year TVF. More importantly, the risk of TVF could be significantly decreased through IVUS guidance in patients with ACS, especially in those who had an IVUS-defined optimal procedure.

OBJECTIVE: To investigate the value of T2 mapping in the assessment of myocardial changes and prognosis in patients with acute ST segment elevation myocardial infarction (STEMI). METHODS: A retrospective study was conducted. A total of 30 patients with acute STEMI admitted to Tianjin First Central Hospital from January 2021 to March 2022 were enrolled as the experimental group. At the same time, 30 age- and sex-matched healthy volunteers and outpatients with non-specific chest pain with no abnormalities in cardiac magnetic resonance (CMR) examination were selected as the control group. CMR was performed within 2 weeks after the diagnosis of STEMI, as the initial reference. A plain CMR review was performed 6 months later (chronic myocardial infarction, CMI). Plain scanning includes film sequence (CINE), T2 weighted short tau inversion recovery (T2-STIR), native-T1 mapping, and T2 mapping. Enhanced scanning includes first-pass perfusion, late gadolinium enhancement (LGE), and post-contrast T1 mapping. Quantitative myocardial parameters were compared between the two groups, before and after STEMI myocardial infarction. The receiver operator characteristic curve (ROC curve) was used to evaluate the diagnostic efficacy of native-T1 before myocardial contrast enhancement and T2 values in differentiating STEMI and CMI after 6 months. RESULTS: There were no statistically significant differences in age, gender, heart rate and body mass index (BMI) between the two groups, which were comparable. The native-T1 value, T2 value and extracellular volume (ECV) were significantly higher than those in the control group [native-T1 value (ms): 1 434.5±165.3 vs. 1 237.0±102.5, T2 value (ms): 48.3±15.6 vs. 21.8±13.1, ECV: (39.6±13.8)% vs. (22.8±5.0)%, all P < 0.05]. In the experimental group, 12 patients were re-examined by plain CMR scan 6 months later. After 6 months, the high signal intensity on T2-STIR was still visible, but the range was smaller than that in the acute phase, and the native-T1 and T2 values were significantly lower than those in the acute phase [native-T1 value (ms): 1 271.0±26.9 vs. 1 434.5±165.3, T2 value (ms): 34.2±11.2 vs. 48.3±15.6, both P < 0.05]. ROC curve analysis showed that the area under the ROC curve (AUC) of native-T1 and T2 values in differentiating acute STEMI from CMI was 0.71 and 0.80, respectively. When native-T1 cut-off value was 1 316.0 ms, the specificity was 100% and the sensitivity was 53.3%; when T2 cut-off value was 46.7 ms, the specificity was 100% and the sensitivity was 73.8%. CONCLUSIONS The T2 mapping is a non-invasive method for the diagnosis of myocardial changes in patients with acute STEMI myocardial infarction, and can be used to to evaluate the clinical prognosis of patients.
Humans ; ST Elevation Myocardial Infarction/diagnosis* ; Contrast Media ; Prognosis ; Retrospective Studies ; Magnetic Resonance Imaging, Cine/methods* ; Gadolinium ; Myocardium/pathology* ; Myocardial Infarction ; Predictive Value of Tests