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 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.
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.
ObjectiveTo investigate the factors associated with unplanned readmission within 30 days after discharge in adult patients who underwent coronary artery bypass grafting (CABG) and to develop and validate a risk prediction model. MethodsA retrospective analysis was conducted on the clinical data of patients who underwent isolated CABG at the Nanjing First Hospital between January 2020 and June 2024. Data from January 2020 to August 2023 were used as a training set, and data from September 2023 to June 2024 were used as a validation set. In the training set, patients were divided into a readmission group and a non-readmission group based on whether they had unplanned readmission within 30 days post-discharge. Clinical data between the two groups were compared, and logistic regression was performed to identify independent risk factors for unplanned readmission. A risk prediction model and a nomogram were constructed, and internal validation was performed to assess the model’s performance. The validation set was used for validation. ResultsA total of 2 460 patients were included, comprising 1 787 males and 673 females, with a median age of 70 (34, 89) years. The training set included 1 932 patients, and the validation set included 528 patients. In the training set, there were statistically significant differences between the readmission group (79 patients) and the non-readmission group (1 853 patients) in terms of gender, age, carotid artery stenosis, history of myocardial infarction, preoperative anemia, and heart failure classification (P<0.05). The main causes of readmission were poor wound healing, postoperative pulmonary infections, and new-onset atrial fibrillation. Multivariable logistic regression analysis revealed that females [OR=1.659, 95%CI (1.022, 2.692), P=0.041], age [OR=1.042, 95%CI (1.011, 1.075), P=0.008], carotid artery stenosis [OR=1.680, 95%CI (1.130, 2.496), P=0.010], duration of first ICU stay [OR=1.359, 95%CI (1.195, 1.545), P<0.001], and the second ICU admission [OR=4.142, 95%CI (1.507, 11.383), P=0.006] were independent risk factors for unplanned readmission. In the internal validation, the area under the curve (AUC) was 0.806, and the net benefit rate of the clinical decision curve analysis (DCA) was >3%. In the validation set, the AUC was 0.732, and the DCA net benefit rate ranged from 3% to 48%. ConclusionFemales, age, carotid artery stenosis, duration of first ICU stay, and second ICU admission are independent risk factors for unplanned readmission within 30 days after isolated CABG. The constructed nomogram demonstrates good predictive power.
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.
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.
Objective To systematically evaluate risk prediction models for 30-day unplanned readmission in patients undergoing coronary artery bypass grafting (CABG). Methods We searched PubMed, EMbase, Cochrane Library, Web of Science, CINAHL, CNKI, CBM, WanFang, and VIP databases from inception to June 25, 2025. Two investigators independently screened literature, extracted data, and assessed bias risk/applicability using PROBAST criteria. Results Thirteen studies comprising 17 prediction models were included. Ten models reported the area under the receiver operating characteristic curve (AUC) for modeling (0.597-0.906), ten models reported the AUC for internal validation (0.57-0.92), and twelve models reported the AUC for external validation (0.537-0.865). Core predictors included age, female sex, diabetes, and heart failure. All studies had a high risk of bias. Conclusion The research on risk prediction models for 30-day unplanned readmission in patients undergoing CABG is still in its exploratory stages. Some models exhibit insufficient performance, and there is a need to enhance the processes of model validation and performance evaluation. It is expected that future efforts will focus on developing prediction models with excellent performance and high applicability, to assist healthcare providers in the early identification of high-risk patients for readmission.
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.
ObjectiveThe re-hospitalization and death events of patients heart failure caused by coronary heart disease are characterized by non-independence, heterogeneity, and censored data. A joint frailty model is established to jointly model the events, explore the risk factors affecting the prognosis of patients, and reduce the re-hospitalization rate and mortality of patients. MethodsThe sample included 4 682 patients with heart failure caused by coronary heart disease in two tertiary hospitals from January 2014 and June 2019. The electronic medical record information of patients during hospitalization and their follow-up information were collected. The Cox model, conditional frailty model and joint frailty model were used to analyze patient re-hospitalization and death. ResultsThe joint frailty model identified patients with a higher risk of both relapse and death (θ=0.209, P<0.001). Risk factors for re-hospitalization were advanced age, grade 3 hypertension, mental work, no medical insurance, high cystatin C, low ejection fraction, and low free thyroxine-3 and thyroxine-4. Antiplatelet drugs and statins significantly reduced the risk of re-hospitalization. Risk factors for death were advanced age, New York Heart Association classification Ⅲ to Ⅳ, no medical insurance, mental work, high cystatin C level, high troponin-I level, low free thyroxine-3, and low ejection fraction. Percutaneous coronary intervention, and taking antiplatelet drugs and statins significantly reduced the risk of death. ConclusionThe joint frailty model can simultaneously model recurring and terminal events, and accurately predict them. Our results suggest that thyroid hormone levels and cystatin C levels of patients should be considered more carefully. People with mental jobs should change bad working habits to reduce adverse outcomes.