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 identify the predictors for readmission in the ICU among cardiac surgery patients. Methods We conducted a retrospective cohort study of 2 799 consecutive patients under cardiac surgery, who were divided into two groups including a readmission group (47 patients, 27 males and 20 females at age of 62.0±14.4 years) and a non readmission group (2 752 patients, 1 478 males and 1 274 females at age of 55.0±13.9 years) in our hospital between January 2014 and October 2016. Results The incidence of ICU readmission was 1.68% (47/2 799). Respiratory disorders were the main reason for readmission (38.3%).Readmitted patients had a significantly higher in-hospital mortality compared to those requiring no readmission (23.4% vs. 4.6%, P<0.001). Logistic regression analysis revealed that pre-operative renal dysfunction (OR=5.243, 95%CI 1.190 to 23.093, P=0.029), the length of stay in the ICU (OR=1.002, 95%CI 1.001 to 1.004, P=0.049), B-type natriuretic peptide (BNP) in the first postoperative day (OR=1.000, 95%CI 1.000 to 1.001, P=0.038), acute physiology and chronic health evaluationⅡ (APACHEⅡ) score in the first 24 hours of admission to the ICU (OR=1.171, 95%CI 1.088 to1.259, P<0.001), and the drainage on the day of surgery (OR=1.001, 95%CI1.001 to 1.002, P<0.001) were the independent risk factors for readmission to the cardiac surgery ICU. Conclusion The early identification of high risk patients for readmission in the cardiac surgery ICU could encourage both more efficient healthcare planning and resources allocation.
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.
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 systematically review the risk prediction model of intensive care unit (ICU) readmissions. MethodsCNKI, WanFang Data, VIP, CBM, PubMed, EMbase, Web of Science and The Cochrane Library databases were electronically searched to collect the related studies on risk prediction models of ICU readmissions from inception to June 12th, 2022. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies; then, the qualitative systematic review was performed. ResultsA total of 15 studies involving 23 risk prediction models were included. The area under the ROC curve of the models was 0.609-0.924. The most common five predictors of the included model were age, length of ICU hospitalization, heart rate, respiration, and admission diagnosis. ConclusionThe overall prediction performance of the risk prediction model of ICU readmissions is good; however, there are differences in research types and outcomes, and the clinical value of the model needs to be further studied.