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.
ObjectiveTo investigate the influencing factors of unplanned readmission in patients with chronic obstructive pulmonary disease (COPD) within 1 year, construct a risk prediction model and evaluate its effect. MethodsClinical data of 403 inpatients with COPD were continuously collected from January 2023 to May 2023, including 170 cases in the readmission group and 233 cases in the non readmission group. LASSO regression was applied to screen the optimized variables and multivariate logistic regression analyses were applied to explore the risk factors of unplanned readmission in patients with COPD within 1 year. After that a nomogram prediction model was constructed and evaluated its discrimination, calibration, and clinical applicability. ResultsThe incidence of unplanned readmission in patients with COPD within 1 year was 42.2%. Respiratory failure, number of acute exacerbation in the last year, creatinine and white blood cell count were risk factors for unplanned admission of patients with COPD within one year (P<0.05). Creatinine, white blood cell count, the number of acute exacerbation in the last year, the course of disease, concomitant respiratory failure and high uric acid were included in the nomogram model, the area under curve (AUC) and its 95% confidential interval (CI) of the nomogram model was 0.687 (0.636 - 0.739), with the sensitivity, specificity, and accuracy were 0.824, 0.742 and 0.603, respectively. The AUC of the nomogram after re-sampling 1 000 times was 0.687 (0.634 - 0.739). The calibration curve showed a high degree of three line overlap and the clinical decision curve showed that the nomogram model provided better net benefits than the treat-all tactics or the treat-none tactics with threshold probabilities of 15.0% - 55.0%. ConclusionThe nomogram model constructed based on creatinine, white blood cell count, the number of acute exacerbation in the last year, the course of disease, concomitant respiratory failure and high uric acid has good predictive value for unplanned readmission in patients with COPD within 1 year.
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.
Objective To construct an information hospital service system and discuss the application effect of information construction in the hospital service center. Methods Patients admitted to West China Hospital of Sichuan University between June 2022 and January 2023 were selected. We innovatively practiced intelligent safety gate, self-appointment admission registration, pre-hospital examination and advance migration, pre-hospital health education, an age-appropriate transformation of information service, and other information service measures to investigate the medical experience of patients, and compared patients’ satisfaction with medical treatment under four admission management methods (Huayitong APP, WeChat, self-service machine, and manual management). Results A total of 1452 patients were surveyed. The overall satisfaction score for medical treatment of patients was (4.98±0.04) points. Among them, Huayitong APP was (4.99±0.03) points, WeChat was (4.98±0.13) points, self-service machine was (4.97±0.05) points, and manual treatment was (4.92±0.11) points. There was a statistically significant difference between groups in overall satisfaction with different admission procedures (F=68.582, P<0.001). Since the information construction of the hospital admission service center was carried out, the average time of admission was (12.4±2.3) minutes, and 89.4% (1 298/1 452) of patients thought the time of admission was ideal. Conclusions The information construction of a hospital admission service center can effectively improve patients’ medical experience and enhance patient satisfaction. In the future, it is necessary to explore the influencing factors of patients’ satisfaction with information construction, and constantly improve and upgrade the information construction of hospital admission service centers.
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 determine the trend in the causes of admission among diabetic patients in West China Hospital from 1996 to 2005. Methods The medical records of diabetic inpatients from January 1996 to December 2005 were retrieved, and half of them were randomly selected. A questionnaire was completed and SPSS13.0 software was used for statistical analyses. Results The most common causes of admission for diabetic patients were diabetic chronic complications (20.2%), infection (19.5%), hyperglycemic symptoms (11.7%), malignant tumor (8.9%) and diabetic acute complications (5.8%). The constituent ratios of diabetic macrovascular disease and malignant tumor as the admission causes tended to increase, while the constituent ratios of diabetic microvascular disease, hyperglycemic symptoms and diabetic acute complications tended to decrease. Infection remained as one of the main causes of admission among diabetic patients. Conclusion The main cause of admission to West China Hospital for diabetic patients from 1996 to 2005 was diabetic chronic complications.
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.
Objective To investigate the impact of nutritional risk on unplanned readmissions in elderly patients with chronic obstructive pulmonary disease (COPD), to provide evidence for clinical nutrition support intervention. Methods Elderly patients with COPD meeting the inclusive criteria and admitted between June 2014 and May 2015 were recruited and investigated with nutritional risk screening 2002 (NRS 2002) and unplanned readmission scale. Meanwhile, the patients’ body height and body weight were measured for calculating body mass index (BMI). Results The average score of nutritional risk screening of the elderly COPD patients was 4.65±1.33. There were 456 (40.07%) patients who had no nutritional risk and 682 (59.93%) patients who had nutritional risk. There were 47 (4.13%) patients with unplanned readmissions within 15 days, 155 (13.62%) patients within 30 days, 265 (23.28%) patients within 60 days, 336 (29.53%) patients within 180 days, and 705 (61.95%) patients within one year. The patients with nutritional risk had significantly higher possibilities of unplanned readmissions within 60 days, 180 days and one year than the patients with no nutritional risk (all P<0.05). The nutritional risk, age and severity of disease influenced unplanned readmissions of the elderly patients with COPD (all P<0.05). Conclusions There is a close correlation between nutritional risk and unplanned readmissions in elderly patients with COPD. Doctors and nurses should take some measures to reduce the nutritional risk so as to decrease the unplanned readmissions to some degree.
ObjectiveTo analyze the influencing factors of acute exacerbation readmission in elderly patients with chronic obstructive pulmonary disease (COPD) within 30 days, construct and validate the risk prediction model.MethodsA total of 1120 elderly patients with COPD in the respiratory department of 13 general hospitals in Ningxia from April 2019 to August 2020 were selected by convenience sampling method and followed up until 30 days after discharge. According to the time of filling in the questionnaire, 784 patients who entered the study first served as the modeling group, and 336 patients who entered the study later served as the validation group to verify the prediction effect of the model.ResultsEducation level, smoking status, number of acute exacerbations of COPD hospitalizations in the past 1 year, regular use of medication, rehabilitation and exercise, nutritional status and seasonal factors were the influencing factors of patients’ readmission to hospital. The risk prediction model was constructed: Z=–8.225–0.310×assignment of education level+0.564×assignment of smoking status+0.873×assignment of number of acute exacerbations of COPD hospitalizations in the past 1 year+0.779×assignment of regular use of medication+0.617×assignment of rehabilitation and exercise +0.970×assignment of nutritional status+assignment of seasonal factors [1.170×spring (0, 1)+0.793×autumn (0, 1)+1.488×winter (0, 1)]. The area under ROC curve was 0.746, the sensitivity was 75.90%, and the specificity was 64.30%. Hosmer-Lemeshow test showed that P=0.278. Results of model validation showed that the sensitivity, the specificity and the accuracy were 69.44%, 85.71% and 81.56%, respectively.ConclusionsEducation level, smoking status, number of acute exacerbations of COPD hospitalizations in the past 1 year, regular use of medication, rehabilitation and exercise, nutritional status and seasonal factors are the influencing factors of patients’ readmission to hospital. The risk prediction model is constructed based on these factor. This model has good prediction effect, can provide reference for the medical staff to take preventive treatment and nursing measures for high-risk patients.