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Abstract Introduction Emerging infectious diseases (EIDs) can disrupt the healthcare system, causing regulatory changes that affect the healthcare-seeking process and potentially increase patient-physician dissatisfaction. This study aimed to collect and analyze patients’ and physicians’ complaints during an EID outbreak to inform potential clues regarding medical quality and patient safety enhancement in future dealing with EIDs, employing text mining methodologies. Methods In this descriptive study, complaint records from January 2020 to February 2023 at West China Hospital, a national medical facility in China, were analyzed. Patient and physician complaints have been retrospectively retrieved from the record from the medical department, and then categorized into distinct groups based on reporting reasons, encompassing COVID-19-related policies, healthcare access, availability of medical resources, and financial concerns. Results During the COVID-19 pandemic, 541 COVID-19-related complaints were identified: 330 (61.00%) from patients and 211 (39.00%) from physicians. The monthly volume of complaints fluctuated, starting at 10 in 2020, peaking at 21 in 2022, and dropping to 14 in 2023. Most complaints from inpatients were expressed by older males aged 40 to 65 (38.82%, 210/541). The primary source of complaints was related to mandatory COVID-19 policies (79.30%, 429/541), followed by concerns regarding timely healthcare services (31.61%, 171/541). Few complaints were expressed regarding the insufficiency of medical resources (2.77%, 15/541) and the high costs (4.25%, 23/541). The frequency of complaints expressed by doctors and patients in the emergency department was higher compared with other departments (24.58%, 133/541). Conclusions Increased complaints may serve as a primitive and timely resource for investigating the potential hazards and drawbacks associated with policies pertaining to EIDs. Prompt collection and systematical analysis of patient and physician feedback could help us accurately evaluate the efficacy and repercussions of these policies. Implementing complaints-based assessment might improve care standards in forthcoming healthcare environments grappling with EIDs.

Abstract Background Surgery in geriatric patients often poses risk of major postoperative complications. Acute kidney injury (AKI) is a common complication following noncardiac surgery and is associated with increased mortality. Early identification of geriatric patients at high risk of AKI could facilitate preventive measures and improve patient prognosis. This study used machine learning methods to identify important features and predict AKI following noncardiac surgery in geriatric patients. Methods The data for this study were obtained from a prospective cohort. Patients aged ≥ 65 years who received noncardiac surgery from June 2019 to December 2021 were enrolled. Data were split into training set (from June 2019 to March 2021) and internal validation set (from April 2021 to December 2021) by time. The least absolute shrinkage and selection operator (LASSO) regularization algorithm and the random forest recursive feature elimination algorithm (RF-RFE) were used to screen important predictors. Models were trained through extreme gradient boosting (XGBoost), random forest, and LASSO. The SHapley Additive exPlanations (SHAP) package was used to interpret the machine learning model. Results The training set included 6753 geriatric patients. Of these, 250 (3.70%) patients developed AKI. The XGBoost model with RF-RFE selected features outperformed other models with an area under the precision-recall curve (AUPRC) of 0.505 (95% confidence interval [CI]: 0.369–0.626) and an area under the receiver operating characteristic curve (AUROC) of 0.806 (95%CI: 0.733–0.875). The model incorporated ten predictors, including operation site and hypertension. The internal validation set included 3808 geriatric patients, and 96 (2.52%) patients developed AKI. The model maintained good predictive performance with an AUPRC of 0.431 (95%CI: 0.331–0.524) and an AUROC of 0.845 (95%CI: 0.796–0.888) in the internal validation. Conclusions This study developed a simple machine learning model and a web calculator for predicting AKI following noncardiac surgery in geriatric patients. This model may be a valuable tool for guiding preventive measures and improving patient prognosis. Trial registration The protocol of this study was approved by the Committee of Ethics from West China Hospital of Sichuan University (2019–473) with a waiver of informed consent and registered at www.chictr.org.cn (ChiCTR1900025160, 15/08/2019).

BackgroundMental health challenges are encountered by frail older adults as the population ages. The extant literature is scant regarding the correlation between depressive symptoms and social participation among frail older adults.MethodsThis study is based on an analysis of data from China Health and Retirement Longitudinal Study (CHARLS) participants aged 60 and older who are frail. A frailty index (FI) was developed for the purpose of assessing the frailty level of the participants. Additionally, latent class analysis (LCA) was employed to classify the participants’ social engagement patterns in 2015 and 2018. The study used ordered logistic regression to examine the relationship between social participation type and depressive symptoms. We also used Latent Transition Analysis (LTA) methods to explore the impact of changes in social activity types on depressive symptoms after three years of follow-up in 2018. In addition, the response surface analysis (RSM) investigation explored the relationship among FI, depression, and social participation.ResultsA total of 4,384 participants completed the baseline survey; three years later, 3,483 were included in the follow-up cohort. The baseline survey indicates that female older adults in rural areas who are single, have lower incomes, shorter sleep durations, and lighter weights exhibited more severe depressive symptoms. Social participation patterns were categorized into five subgroups by LCA. The findings indicate that individuals classified as “board game enthusiasts” (OR, 0.62; 95% CI, 0.47-0.82) and those as “extensive social interaction” (OR,0.67; 95% CI, 0.49-0.90) have a significantly lower likelihood of developing depressive symptoms compared to the “socially isolated” group. We also discovered that “socially isolated” baseline participants who transitioned to the “helpful individual” group after three years had significantly greater depressed symptoms (OR, 1.56; 95% CI, 1.00-2.44). More social activity types and less FI are linked to lower depression in our study.ConclusionThe results of the study emphasize the importance of social participation patterns and the number of social participation types in relation to the severity of depression among frail older adults individuals. This study’s findings may provide important insights for addressing depressive symptoms in frail older adults person.