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.

Protein kinase Cdc7 supports viral replication by phosphorylating Avibirnavirus VP3 protein.

IMPORTANCE: The Avibirnavirus infectious bursal disease virus is still an important agent which largely threatens global poultry farming industry economics. VP3 is a multifunctional scaffold structural protein that is involved in virus morphogenesis and the regulation of diverse cellular signaling pathways. However, little is known about the roles of VP3 phosphorylation during the IBDV life cycle. In this study, we determined that IBDV infection induced the upregulation of Cdc7 expression and phosphorylated the VP3 Ser13 site to promote viral replication. Moreover, we confirmed that the negative charge addition of phosphoserine on VP3 at the S13 site was essential for IBDV proliferation. This study provides novel insight into the molecular mechanisms of VP3 phosphorylation-mediated regulation of IBDV replication.

Long Noncoding RNA AROD Inhibits Host Antiviral Innate Immunity via the miR-324-5p-CUEDC2 Axis.

Long noncoding RNAs (lncRNAs) are a class of noncoding RNAs that are involved in multiple biological processes. Here, we report a mechanism through which the lnc-AROD-miR-324-5p-CUEDC2 axis regulates the host innate immune response, using influenza A virus (IAV) as a model. We identified that host lnc-AROD without protein-coding capability is composed of 975 nucleotides. Moreover, lnc-AROD inhibited interferon-ß expression, as well as interferon-stimulated genes ISG15 and MxA. Furthermore, in vivo assays confirmed that lnc-AROD overexpression increased flu virus pathogenicity and mortality in mice. Mechanistically, lnc-AROD interacted with miR-324-5p, leading to decreased binding of miR-324-5p to CUEDC2. Collectively, our findings demonstrated that lnc-AROD is a critical regulator of the host antiviral response via the miR-324-5p-CUEDC2 axis, and lnc-AROD functions as competing endogenous RNA. Our results also provided evidence that lnc-AROD serves as an inhibitor of the antiviral immune response and may represent a potential drug target. IMPORTANCE lnc-AROD is a potential diagnostic and discriminative biomarker for different cancers. However, so far the mechanisms of lnc-AROD regulating virus replication are not well understood. In this study, we identified that lnc-AROD is downregulated during RNA virus infection. We demonstrated that lnc-AROD enhanced CUEDC2 expression, which in turn inhibited innate immunity and favored IAV replication. Our studies indicated that lnc-AROD functions as a competing endogenous RNA that binds miR-324-5p and reduces its inhibitory effect on CUEDC2. Taken together, our findings reveal that lnc-AROD plays an important role during the host antiviral immune response.

A nomogram to predict postoperative infection for older hip fracture patients.

INTRODUCTION: Postoperative infection is one of the most common postoperative complications in hip fracture surgery. It is related with increased morbidity and mortality. This study aimed at developing a nomogram to predict the individual probability of postoperative infection to facilitate perioperative decision-making. MATERIALS AND METHODS: In this retrospective study, we included all patients over 65 years old admitted for hip fracture in West China Hospital of Sichuan University from 1 January 2015 to 31 December 2019. Univariate and multivariate logistic regression analyses were used to identify significant predictors. We used all-subsets regression to screen an optimal model, and visualized the model through drawing nomogram. To evaluate the model performance, we applied receiver operating characteristic curve and calibration curve. RESULTS: We enrolled 677 older patients. 136 (20.1%) patients developed postoperative infection during hospitalization. Variables retained in the final model were albumin [odds ratio (OR) 0.90, 95% confidence interval (CI) 0.84-0.96], cholesterol (OR 1.49, 95% CI 1.04-2.15), blood phosphorus (OR 0.16, 95% CI 0.05-0.48), high-density lipoprotein (OR 0.42, 95% CI 0.19-0.89), surgery type (OR 2.27, 95% CI 1.35-3.90), smoking (OR 1.95, 95% CI 1.02-3.66), American Society of Anesthesiologists classification [class III (OR 1.02, 95% CI 0.55-1.93); class IV (OR 1.93, 95% CI 0.76-4.82)], and chronic pulmonary disease (OR 2.16, 95% CI 1.25-3.68). The C-index of the nomogram was 0.752 (95% CI 0.697-0.806). Calibration curve showed good agreement between predicted value and observed outcome. In the validation group, our nomogram showed an area under the receiver operating characteristic curve of 0.723 (95% CI 0.639-0.807). CONCLUSION: Our nomogram showed good discrimination ability in predicting individual probability of postoperative infection among older patients with hip fracture surgery. The nomogram could help clinicians identify patients at high risk of postoperative infection before surgery.

Machine learning prediction of postoperative major adverse cardiovascular events in geriatric patients: a prospective cohort study.

BACKGROUND: Postoperative major adverse cardiovascular events (MACEs) account for more than one-third of perioperative deaths. Geriatric patients are more vulnerable to postoperative MACEs than younger patients. Identifying high-risk patients in advance can help with clinical decision making and improve prognosis. This study aimed to develop a machine learning model for the preoperative prediction of postoperative MACEs in geriatric patients. METHODS: We collected patients' clinical data and laboratory tests prospectively. All patients over 65 years who underwent surgeries in West China Hospital of Sichuan University from June 25, 2019 to June 29, 2020 were included. Models based on extreme gradient boosting (XGB), gradient boosting machine, random forest, support vector machine, and Elastic Net logistic regression were trained. The models' performance was compared according to area under the precision-recall curve (AUPRC), area under the receiver operating characteristic curve (AUROC) and Brier score. To minimize the influence of clinical intervention, we trained the model based on undersampling set. Variables with little contribution were excluded to simplify the model for ensuring the ease of use in clinical settings. RESULTS: We enrolled 5705 geriatric patients into the final dataset. Of those patients, 171 (3.0%) developed postoperative MACEs within 30 days after surgery. The XGB model outperformed other machine learning models with AUPRC of 0.404(95% confidence interval [CI]: 0.219-0.589), AUROC of 0.870(95%CI: 0.786-0.938) and Brier score of 0.024(95% CI: 0.016-0.032). Model trained on undersampling set showed improved performance with AUPRC of 0.511(95% CI: 0.344-0.667, p < 0.001), AUROC of 0.912(95% CI: 0.847-0.962, p < 0.001) and Brier score of 0.020 (95% CI: 0.013-0.028, p < 0.001). After removing variables with little contribution, the undersampling model showed comparable predictive accuracy with AUPRC of 0.507(95% CI: 0.338-0.669, p = 0.36), AUROC of 0.896(95%CI: 0.826-0.953, p < 0.001) and Brier score of 0.020(95% CI: 0.013-0.028, p = 0.20). CONCLUSIONS: In this prospective study, we developed machine learning models for preoperative prediction of postoperative MACEs in geriatric patients. The XGB model showed the best performance. Undersampling method achieved further improvement of model performance. TRIAL REGISTRATION: The protocol of this study was registered at www.chictr.org.cn (15/08/2019, ChiCTR1900025160).

A multicenter prospective study on postoperative pulmonary complications prediction in geriatric patients with deep neural network model.

Aim: Postoperative pulmonary complications (PPCs) can increase the risk of postoperative mortality, and the geriatric population has high incidence of PPCs. Early identification of high-risk geriatric patients is of great value for clinical decision making and prognosis improvement. Existing prediction models are based purely on structured data, and they lack predictive accuracy in geriatric patients. We aimed to develop and validate a deep neural network model based on combined natural language data and structured data for improving the prediction of PPCs in geriatric patients. Methods: We consecutively enrolled patients aged ≥65 years who underwent surgery under general anesthesia at seven hospitals in China. Data from the West China Hospital of Sichuan University were used as the derivation dataset, and a deep neural network model was developed based on combined natural language data and structured data. Data from the six other hospitals were combined for external validation. Results: The derivation dataset included 12,240 geriatric patients, and 1949(15.9%) patients developed PPCs. Our deep neural network model outperformed other machine learning models with an area under the precision-recall curve (AUPRC) of 0.657(95% confidence interval [CI], 0.655-0.658) and an area under the receiver operating characteristic curve (AUROC) of 0.884(95% CI, 0.883-0.885). The external dataset included 7579 patients, and 776(10.2%) patients developed PPCs. In external validation, the AUPRC was 0.632(95%CI, 0.632-0.633) and the AUROC was 0.889(95%CI, 0.888-0.889). Conclusions: This study indicated that the deep neural network model based on combined natural language data and structured data could improve the prediction of PPCs in geriatric patients.

Ubiquitination Is Essential for Avibirnavirus Replication by Supporting VP1 Polymerase Activity.

Ubiquitination is critical for several cellular physical processes. However, ubiquitin modification in virus replication is poorly understood. Therefore, the present study aimed to determine the presence and effect of ubiquitination on polymerase activity of viral protein 1 (VP1) of avibirnavirus. We report that the replication of avibirnavirus is regulated by ubiquitination of its VP1 protein, the RNA-dependent RNA polymerase of infectious bursal disease virus (IBDV). In vivo detection revealed the ubiquitination of VP1 protein in IBDV-infected target organs and different cells but not in purified IBDV particles. Further analysis of ubiquitination confirms that VP1 is modified by K63-linked ubiquitin chain. Point mutation screening showed that the ubiquitination site of VP1 was at the K751 residue in the C terminus. The K751 ubiquitination is independent of VP1's interaction with VP3 and eukaryotic initiation factor 4A II. Polymerase activity assays indicated that the K751 ubiquitination at the C terminus of VP1 enhanced its polymerase activity. The K751-to-R mutation of VP1 protein did not block the rescue of IBDV but decreased the replication ability of IBDV. Our data demonstrate that the ubiquitination of VP1 is crucial to regulate its polymerase activity and IBDV replication.IMPORTANCE Avibirnavirus protein VP1, the RNA-dependent RNA polymerase, is responsible for IBDV genome replication, gene expression, and assembly. However, little is known about its chemical modification relating to its polymerase activity. In this study, we revealed the molecular mechanism of ubiquitin modification of VP1 via a K63-linked ubiquitin chain during infection. Lysine (K) residue 751 at the C terminus of VP1 is the target site for ubiquitin, and its ubiquitination is independent of VP1's interaction with VP3 and eukaryotic initiation factor 4A II. The K751 ubiquitination promotes the polymerase activity of VP1 and unubiquitinated VP1 mutant IBDV significantly impairs virus replication. We conclude that VP1 is the ubiquitin-modified protein and reveal the mechanism by which VP1 promotes avibirnavirus replication.