Purpose The purpose of this study was to analyze and compare the clinical characteristics of patients with 30-day planned and unplanned readmissions and to identify patients at high risk for unplanned readmissions. This will facilitate a better understanding of these readmissions and improve and optimize resource utilization for this patient population. Methods A retrospective cohort descriptive study was conducted at the West China Hospital (WCH), Sichuan University from January 1, 2015, to December 31, 2020. Discharged patients (≥ 18 years old) were divided into unplanned readmission and planned readmission groups according to 30-day readmission status. Demographic and related information was collected for each patient. Logistic regression analysis was used to assess the association between unplanned patient characteristics and the risk of readmission. Results We identified 1,118,437 patients from 1,242,496 discharged patients, including 74,494 (6.7%) 30-day planned readmissions and 9,895 (0.9%) unplanned readmissions. The most common diseases of planned readmissions were antineoplastic chemotherapy (62,756/177,749; 35.3%), radiotherapy sessions for malignancy (919/8,229; 11.2%), and systemic lupus erythematosus (607/4,620; 13.1%). The most common diseases of unplanned readmissions were antineoplastic chemotherapy (2038/177,747; 1.1%), age-related cataract (1061/21,255; 5.0%), and unspecified disorder of refraction (544/5,134; 10.6%). There were statistically significant differences between planned and unplanned readmissions in terms of patient sex, marital status, age, length of initial stay, the time between discharge, ICU stay, surgery, and health insurance. Conclusion Accurate information on 30-day planned and unplanned readmissions facilitates effective planning of healthcare resource allocation. Identifying risk factors for 30-day unplanned readmissions can help develop interventions to reduce readmission rates.
Disseminated intravascular coagulation (DIC) is a complex, life-threatening syndrome associated with the end-stage of different coagulation disorders. Early prediction of the risk of DIC development is an urgent clinical need to reduce adverse outcomes. However, effective approaches and models to identify early DIC are still lacking. In this study, a novel interpretable deep learning based time series is used to predict the risk of DIC. The study cohort included ICU patients from a 4300-bed academic hospital between January 1, 2019, and January 1, 2022. Experimental results show that our model achieves excellent performance (AUC: 0.986, Accuracy: 95.7%, and F1:0.935). Gradient-weighted Class Activation Mapping (Grad-CAM) was used to explain how predictive models identified patients with DIC. The decision basis of the model was displayed in the form of a heat map. The model can be used to identify high-risk patients with DIC early, which will help in the early intervention of DIC patients and improve the treatment effect.
Purpose: To compare and analyze the clinical characteristics and influencing factors of planned and unplanned readmissions within 30 days to provide a basis for the quality of care management.Methods: We searched for inpatients at West China Hospital from January 1, 2015, to December 31, 2020. Patients were divided into unplanned readmission and planned readmission groups according to 30-day readmission status. Patient demographics and related information were collected for each patient.Results: We identified 1,242,496 hospitalized patients, including 74,494 (6.0%) 30-day planned readmissions and 9,895 (0.8%) unplanned readmissions. There were statistically significant differences between planned and unplanned readmissions in terms of patient gender, marital status, age, length of initial stay, the time between discharge, stay in ICU, surgery, and health insurance.Conclusion: Accurate information on 30-day planned and unplanned readmissions facilitates effective planning of healthcare resource allocation. Identify risk factors for 30-day unplanned readmission and develop interventions to reduce readmission rates.
Objective This retrospective cohort study aimed to identify reasons and risk factors associated with 30-day readmission after otolaryngology-head and neck surgery and propose preventive measures. Design The study was conducted at a large single academic tertiary care center in China, analyzing cases of inpatient otolaryngology-head and neck surgery from August 2019 to December 2021. Setting The study was conducted in a large tertiary-care hospital in China. Participants The study included adult patients who underwent otolaryngology-head and neck surgery and experienced 30-day readmissions. Main outcome measures The main outcome measured was the analysis of 30-day readmissions for adult patients after otolaryngology-head and neck surgery. Results A total of 7,608 otolaryngology-head and neck surgery patients were identified, with 0.85% and 0.84% experiencing unplanned and planned readmissions within 30 days, respectively. Patients with unplanned readmissions were older and had a longer length of stay compared to those with planned readmissions and those without readmissions. Old age and length of stay were identified as risk factors for unplanned readmission. The most common reasons for unplanned and planned readmissions were surgical complications and surgical cancellations, respectively. Conclusions Analyzing the causes and risk factors for 30-day readmissions after otolaryngology-head and neck surgery can guide perioperative planning and help prevent readmissions, leading to improved patient outcomes.
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