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.
The implantation of left ventricular assist device (LVAD) has significantly improved the quality of life for patients with end-stage heart failure. However, it is associated with the risk of complications, with unplanned readmissions gaining increasing attention. This article reviews the influencing factors, prediction methods and models, and intervention measures for unplanned readmissions in LVAD patients, aiming to provide scientific guidance for clinical practice, assist healthcare professionals in accurately assessing patients' conditions, and develop rational care plans.
ObjectiveTo understand the current situation of unplanned readmission of colorectal cancer patients within 30 days after discharge under the enhanced recovery after surgery (ERAS) mode, and to explore the influencing factors.MethodsFrom May 7, 2018 to May 29, 2020, 315 patients with colorectal cancer treated by Department of Gastrointestinal Surgery, West China Hospital, Sichuan University and managed by ERAS process during perioperative period were prospectively selected as the research objects. The general data, clinical disease data and discharge readiness of patients were obtained by questionnaire and electronic medical record. Telephone follow-up was used to find out whether the patient had unplanned readmission 30 days after discharge and logistic regression was used to analyze the influencing factors of unplanned readmission within 30 days after discharge.ResultsWithin 30 days after discharge, 37 patients were admitted to hospital again, the unplanned readmission rate was 11.7%. The primary cause of readmission was wound infection. Logistic regression analysis showed that the body mass decreased by more than 10% in recent half a year (OR=2.611, P=0.031), tumor location in rectum (OR=3.739, P=0.026), operative time ≤3 hours (OR=0.292, P=0.004), and discharge readiness (OR=0.967, P<0.001) were independent predictors of unplanned readmission.ConclusionsUnder the ERAS mode, the readmission rate of colorectal cancer patients within 30 days after discharge is not optimistic. Attention should be focused on patients with significant weight loss, rectal cancer, more than 3 hours of operative time, and low readiness for discharge. Among them, the patient’s body weight and discharge readiness are the factors that can be easily improved by clinical intervention. It can be considered as a new way to reduce the rate of unplanned readmission by improving the patients’ physical quality and carrying out discharge care program.
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.
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.
Objective To analyze the influencing factors of unplanned readmission for day surgery patients under the centralized management mode, and to provide a scientific basis for improving the medical quality and safety of day surgery. Methods The data of patients in the day surgery ward of the Second Affiliated Hospital Zhejiang University School of Medicine between October 2017 and October 2021 were retrospectively collected, and they were divided into an unplanned readmission group and a control group according to whether they were unplanned readmission within 31 days. Multivariate logistic regression model was used to analyze the influencing factors of patients’ unplanned readmission within 31 days. Results There were 30 636 patients, of which 46 were unplanned readmission patients, accounting for 0.15%. Logistic regression analysis showed that male [odds ratio (OR)=0.425, 95% confidence interval (CI) (0.233, 0.776), P=0.005], thyroid surgery [OR=19.938, 95%CI (7.829, 50.775), P<0.001], thoracoscopic partial lobectomy [OR=13.481, 95%CI (5.835, 31.148), P<0.001], laparoscopic cholecystectomy [OR=10.593, 95%CI (3.918, 28.641), P<0.001] and hemorrhoidectomy [OR=13.301, 95%CI (4.473, 39.550), P<0.001] were risk factors for unplanned readmission in patients undergoing day surgery. Conclusion Medical staff in day surgery wards need to strengthen supervision of male patients and high risk surgical patients, and improve patients’ awareness of recovery, so as to reduce the rate of unplanned readmission.
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 systematically evaluate the predictive models for re-admission in patients with heart failure (HF) in China. MethodsStudies related to the risk prediction model for HF patient re-admission published in The Cochrane Library, PubMed, EMbase, CNKI, and other databases were searched from their inception to April 30, 2024. The prediction model risk of bias assessment tool was used to assess the risk of bias and applicability of the included literature, relevant data were extracted to evaluate the model quality. ResultsNineteen studies were included, involving a total of 38 predictive models for HF patient re-admission. Comorbidities such as diabetes, N-terminal pro B-type natriuretic peptide/brain natriuretic peptide, chronic renal insufficiency, left ventricular ejection fraction, New York Heart Association cardiac function classification, and medication adherence were identified as primary predictors. The area under the receiver operating characteristic curve ranged from 0.547 to 0.962. Thirteen studies conducted internal validation, one study conducted external validation, and five studies performed both internal and external validation. Seventeen studies evaluated model calibration, while five studies assessed clinical feasibility. The presentation of the models was primarily in the form of nomograms. All studies had a high overall risk of bias. ConclusionMost predictive models for HF patient re-admission in China demonstrate good discrimination and calibration. However, the overall research quality is suboptimal. There is a need to externally validate and calibrate existing models and develop more stable and clinically applicable predictive models to assess the risk of HF patient re-admission and identify relevant patients for early intervention.