ObjectiveTo systematically review the risk prediction models for readmission within 30 days after discharge in patients with chronic obstructive pulmonary disease (COPD), and provide a reference for clinical selection of risk assessment tools. MethodsDatabases including CNKI, Wanfang Data, VIP, CBM, PubMed, Embase, Web of Science, and Cochrane Library were searched for literature on this topic. The search time was from the inception of the database to April 25, 2023. Literature screening and data extraction were performed by two researchers independently. The risk of bias and applicability of the included literature were evaluated using the risk of bias assessment tool for predictive model studies. ResultsA total of 8 studies were included, including 14 risk prediction models for 30-day readmission of COPD patients after discharge. The total sample size was 125~8 263, the number of outcome events was 24~741, and the area under the receiver operating characteristic curve was 0.58~0.918. The top five most common predictors included in the model were smoking, comorbidities, age, education level, and home oxygen therapy. Although five studies had good applicability, all eight studies had a certain risk of bias. This is mainly due to the small sample size of the model, lack of reporting of blinding, lack of external validation, and inappropriate handling of missing data. ConclusionThe overall prediction performance of the risk prediction model for 30-day readmission of patients with COPD after discharge is good, but the overall research quality is low. In the future, the model should be continuously improved to provide a scientific assessment tool for the early clinical identification of patients with COPD at high risk of readmission within 30 days after discharge.
ObjectivesBased on the historical data of inpatients, a logistic regression model was established. It aimed to identify the influencing factors of patient's admission scheduling decisions and compare them with the actual scheduling rules, so as to discover the differences and deficiencies.MethodsWe extracted data of outpatients and inpatients in Department of Respiration in West China Hospital of Sichuan University from January 1st, 2016 to December 31st, 2016, and standardized the original dataset. We established the binary multivariate logistic regression model through R software and ‘glm’ package.ResultsThe analysis of multi-factor logistic regression showed that the effect of the five variables (type of medical insurance, time of registration, waiting time, type of disease and admission priority) on patient schedule was statistically significant.ConclusionsThe logistic regression model constructed in this study has a good effect on patient planning, which is helpful to provide decision support for admission schedule through identification factors.
Objective To systematically evaluate the efficacy of telemedicine on patients with chronic heart failure. Methods We performed a computerized search of Web of Science, Embase, PubMed, Cochrane Library, China Biomedical Database (SinoMed), CNKI, Wanfang, and VIP databases for studies regarding telemedicine interventions for patients with chronic heart failure from their inception to November 5, 2025. Two reviewers independently conducted study screening, and data extraction. Risk of bias assessment for the included studies was performed using the Cochrane ROB 2.0 tool. Meta-analysis was performed using Review Manager 5.3 and Stata 17.0 software. Results A total of 39 randomized controlled trials (RCTs) involving 13 979 patients were included. All studies were rated as Grade A or B. The meta-analysis results showed that the intervention group had significantly lower rates of all-cause readmission [OR=0.63, 95%CI (0.50, 0.80), P<0.001], heart failure-related readmission [OR=0.50, 95%CI (0.38, 0.64), P<0.001], cardiovascular-related readmission [OR=0.55, 95%CI (0.38, 0.79), P=0.001], and heart failure-related mortality [OR=0.69, 95%CI (0.55, 0.88), P=0.003] compared to the control group. The quality of life [SMD=–1.05, 95%CI (–1.61, –0.49), P<0.001] and self-care ability [SMD=–1.53, 95%CI (–2.19, –0.86), P<0.001] in the intervention group were significantly better than those in the control group. There was no statistically significant difference in all-cause mortality between the two groups (P>0.05). Conclusion Telemedicine interventions can effectively reduce readmission rates and heart failure-related mortality in patients with chronic heart failure and have a positive effect on improving their quality of life and self-care ability. However, it has no significant effect on all-cause mortality. More large-sample RCTs with long-term follow-up are needed to further validate the impact of telemedicine on all-cause mortality in patients with heart failure.
Objective To investigate the transferring methods of earthquake casualties accepted by the Department of Emergency, discuss the requirement for rescue materials in pre-hospital transference and provide information for transferring casualties after disasters in future. Methods Traumatic types and conditions of the wounded admitted by the Department of Emergency of West China Hospital within 3 weeks after Wenchuan earthquake,were collected. The characteristics of the wounded transferred by ambulances and helicopters were analyzed. Results Of the 2 338 wounded, ambulances transferred the most accounting for 60.56%, helicopter transferred 13.47%, and the other transport modes took up 25.96%. As for the macrotraumas, ambulances transferred more than helicopter and other transport mode did (Plt;0.05), while there was no statistical significance between helicopters and other transport modes(Pgt;0.05). Conclusion After the disaster, a field first-aid command system should be immediately established, casualties should be triaged concisely, an appropriate transference mode should be decided according to the degree of injuries and sufficient rescue materials should be provided based on different transference modes.
ObjectiveTo explore the distribution of multidrug resistant organism in neonates admitted to the hospital through various ways, and analyze the risk factors in order to avoid cross infection of multidrug resistant organism in neonatology department.
MethodsA total of 2 124 neonates were monitored from January 2012 to July 2013, among which 1 119 were admitted from outpatient department (outpatient group), 782 were transferred from other departments (other department group), and 223 were from other hospitals (other hospital group). We analyzed their hospital stays, weight, average length of stay, and drug-resistant strains, and their relationship with nosocomial infection.
ResultsAmong the 105 drug-resistant strains, there were 57 from the outpatient group, 27 from the other department group, and 21 from the other hospital group. The positive rate in the patients transferred from other hospitals was the highest (9.42%). Neonates with the hospital stay of more than 14 days and weighing 1 500 g or less were the high-risk groups of drug-resistant strains in nosocomial infection. Drug-resistant strains of nosocomial infection detected in the patients admitted through different ways were basically identical.
ConclusionWe should strengthen screening, isolation, prevention and control work in the outpatient neonate. At the same time, we can't ignore the prevention and control of the infection in neonates from other departments or hospitals, especially the prevention and control work in neonates with the hospital stay of more than 14 days and weighing 1 500 g or less to reduce the occurrence of multiple drug-resistant strains cross infection.
ObjectiveTo explore a method for establishing a priority-scoring model for thyroid carcinoma patient admission. MethodsA questionnaire survey was conducted among specialists and outpatients in the thyroid surgery department of the hospital. The weight coefficient of the index factors was calculated to establish the priority-scoring mode by the analytic hierarchy process. The differences in results between specialists and patients were compared. The logical rationality of the model index was tested. ResultsA priority-scoring model for thyroid carcinoma surgery admission was established, including 10 first-level indicators, such as sex, age, cancer type and TNM stage. The weight coefficients of the indicators from high to low were cancer type (0.137), TNM stage (0.134), tumor size (0.127), tumor invasion degree (0.126), tumor invasion site (0.124), relationship between tumor and capsule (0.111), age (0.093), sex (0.061), place of residence (0.05) and medical insurance type (0.035). After the total ratio test, the model CR value was 0.0073, and the model index was highly rational. ConclusionThis study successfully establish a priority-scoring model for thyroid carcinoma surgery admission, which can provide references and a basis for tiered medical services and relevant researches in the future.
ObjectivesTo investigate risk factors for unplanned readmission in ischemic stroke patients within 31 days by using random forest algorithm.MethodsThe record of readmission patients with ischemic stroke within 31 days from 24 hospitals in Beijing between between 2015 and 2016 were collected. Patients were divided into two groups according to the occurrence of readmission within 31 days or not. Chi-squared or Mann-Whitney U test was used to select variables into the random forest algorithm. The precision coefficient and the Gini coefficient were used to comprehensively assess the importance of all variables, and select the more important variables and use the margind effect to assess relative risk of different levels.ResultsA total of 3 473 patients were included, among them 960 (27.64%) were readmitted within 31 days after stroke hospitalization. Based on the result of random forest, the most important variables affecting the risk of unplanned readmission within 31 days included the length of hospital stay, age, medical expense payment, rank of hospital, and occupation. When hospitalization was within 1 month, 10-day-hospitalization-stay patients had the lowest risk of rehospitalization; the younger the patients was, the higher the risk of readmission was. For ranks of hospital, patients from tertiary hospital had higher risk than secondary hospital. Furthermore, patients whose medical expenses were paid by free medical service and whose occupations were managers or staffs had higher risk of readmission within 31 days.ConclusionsThe unplanned readmission risk within 31 days of discharged ischemic stroke patients was connected not only with disease, but also with personal social and economic factors. Thus, more attention should be paid to both the medical process and the personal and family factors of stroke patients.
