What are the frequent complaints voiced by inpatients and physicians amidst the emerging infectious diseases? - An illustrative instance of the COVID-19.

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.

PPCRKB: a risk factor knowledge base of postoperative pulmonary complications.

Postoperative pulmonary complications (PPCs) are highly heterogeneous disorders with diverse risk factors frequently occurring after surgical interventions, resulting in significant financial burdens, prolonged hospitalization and elevated mortality rates. Despite the existence of multiple studies on PPCs, a comprehensive knowledge base that can effectively integrate and visualize the diverse risk factors associated with PPCs is currently lacking. This study aims to develop an online knowledge platform on risk factors for PPCs (Postoperative Pulmonary Complications Risk Factor Knowledge Base, PPCRKB) that categorizes and presents the risk and protective factors associated with PPCs, as well as to facilitate the development of individualized prevention and management strategies for PPCs based on the needs of each investigator. The PPCRKB is a novel knowledge base that encompasses all investigated potential risk factors linked to PPCs, offering users a web-based platform to access these risk factors. The PPCRKB contains 2673 entries, 915 risk factors that have been categorized into 11 distinct groups. These categories include habit and behavior, surgical factors, anesthetic factors, auxiliary examination, environmental factors, clinical status, medicines and treatment, demographic characteristics, psychosocial factors, genetic factors and miscellaneous factors. The PPCRKB holds significant value for PPC research. The inclusion of both quantitative and qualitative data in the PPCRKB enhances the ability to uncover new insights and solutions related to PPCs. It could provide clinicians with a more comprehensive perspective on research related to PPCs in future. Database URL: http://sysbio.org.cn/PPCs.

A simple machine learning model for the prediction of acute kidney injury following noncardiac surgery in geriatric patients: a prospective cohort study.

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).

Combination of Creatinine with Inflammatory Biomarkers (PCT, CRP, hsCRP) for Predicting Postoperative ICU Admissions for Elderly Patients.

INTRODUCTION: The number of elderly patients who require surgery as their primary treatment has increased rapidly in recent years. Among 300 million people globally who underwent surgery every year, patients aged 65 years and over accounted for more than 30% of cases. Despite medical advances, older patients remain at higher risk of postoperative complications. Early diagnosis and effective prediction are essential requirements for preventing serious postoperative complications. In this study, we aim to provide new biomarker combinations to predict the incidence of postoperative intensive care unit (ICU) admissions > 24 h in elderly patients. METHODS: This investigation was conducted as a nested case-control study, incorporating 413 participants aged ≥ 65 years who underwent non-cardiac, non-urological elective surgeries. These individuals underwent a 30-day postoperative follow-up. Before surgery, peripheral venous blood was collected for analyzing serum creatinine (Scr), procalcitonin (PCT), C-reactive protein (CRP), and high-sensitivity CRP (hsCRP). The efficacy of these biomarkers in predicting postoperative complications was evaluated using receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) values. RESULTS: Postoperatively, 10 patients (2.42%) required ICU admission. Regarding ICU admissions, the AUCs with 95% confidence intervals (CIs) for the biomarker combinations of Scr × PCT and Scr × CRP were 0.750 (0.655-0.845, P = 0.007) and 0.724 (0.567-0.882, P = 0.015), respectively. Furthermore, cardiovascular events were observed in 14 patients (3.39%). The AUC with a 95% CI for the combination of Scr × CRP in predicting cardiovascular events was 0.688 (0.560-0.817, P = 0.017). CONCLUSION: The innovative combinations of biomarkers (Scr × PCT and Scr × CRP) demonstrated efficacy as predictors for postoperative ICU admissions in elderly patients. Additionally, the Scr × CRP also had a moderate predictive value for postoperative cardiovascular events. TRIAL REGISTRATION: China Clinical Trial Registry, ChiCTR1900026223.

Metabolic change in monocytes and postoperative morbidity after major abdominal surgery in elderly patients: A prospective cohort study.

Background: Postoperative complications in aging patients remain a significant cause of increased costs, hospital length of stay, and patient distress. Although alterations in energy metabolism have been closely linked to aging process and surgery, it is still unclear whether metabolic changes during surgery is associated with postoperative complications in elderly patients. This study was conducted to investigate whether metabolic changes during surgery predicts postoperative complications in elderly patients. Methods: We conducted a prospective single-center observational cohort study. 244 adults (aged ≥65 years) who were scheduled for elective major non-cardiac surgery were recruited. Blood samples for each patient were taken before and after surgery. All patients were randomly divided into two groups (122 in each group), then oxygen consumption rate (OCR) or extracellular acidification rate (ECAR) was measured on isolated monocytes in each group. Results: 14 of 110 (12.7%) patients went through OCR measurement and 15 of 122 patients (12.3%) went through ECAR measurement experienced moderate to severe complications. Overall, there was an intensification of glycolysis in monocytes after surgery. Among all variables, only the change (preoperative -postoperative) of glycolytic reserve (GR)/glycolysis (G) and GR/non-glycolytic acidification (NG) were predictors of moderate to severe complications (AUC = 0.70; 95% CI, 0.56-0.81; P = 0.019 and AUC = 0.67; 95% CI, 0.55-0.80; P = 0.031). Decreased postoperative GR/G were associated with worse postoperative complications (RR = 9.08; 95% CI, 1.23-66.81; P = 0.024). Conclusions: Compared with mitochondria function, the change of glycolytic function in monocyte was more valuable in predicting postoperative complications after major abdominal surgery. Our study gave us a new insight into identifying patients at high risk in aging patients.

Utilising intraoperative respiratory dynamic features for developing and validating an explainable machine learning model for postoperative pulmonary complications.

BACKGROUND: Timely detection of modifiable risk factors for postoperative pulmonary complications (PPCs) could inform ventilation strategies that attenuate lung injury. We sought to develop, validate, and internally test machine learning models that use intraoperative respiratory features to predict PPCs. METHODS: We analysed perioperative data from a cohort comprising patients aged 65 yr and older at an academic medical centre from 2019 to 2023. Two linear and four nonlinear learning models were developed and compared with the current gold-standard risk assessment tool ARISCAT (Assess Respiratory Risk in Surgical Patients in Catalonia Tool). The Shapley additive explanation of artificial intelligence was utilised to interpret feature importance and interactions. RESULTS: Perioperative data were obtained from 10 284 patients who underwent 10 484 operations (mean age [range] 71 [65-98] yr; 42% female). An optimised XGBoost model that used preoperative variables and intraoperative respiratory variables had area under the receiver operating characteristic curves (AUROCs) of 0.878 (0.866-0.891) and 0.881 (0.879-0.883) in the validation and prospective cohorts, respectively. These models outperformed ARISCAT (AUROC: 0.496-0.533). The intraoperative dynamic features of respiratory dynamic system compliance, mechanical power, and driving pressure were identified as key modifiable contributors to PPCs. A simplified model based on XGBoost including 20 variables generated an AUROC of 0.864 (0.852-0.875) in an internal testing cohort. This has been developed into a web-based tool for further external validation (https://aorm.wchscu.cn/). CONCLUSIONS: These findings suggest that real-time identification of surgical patients' risk of postoperative pulmonary complications could help personalise intraoperative ventilatory strategies and reduce postoperative pulmonary complications.