Background: The association between malnutrition and postoperative acute kidney injury (AKI) has not been well studied. In this study, the authors examined the association between preoperative nutritional status and postoperative AKI in older patients who underwent major abdominal surgery, as well as the predictive value of malnutrition for AKI. Materials and methods: The authors retrospectively included patients aged 65 or older who underwent major elective abdominal surgery. The nutritional status of the patient was evaluated using three objective nutritional indices, such as the geriatric nutritional risk index (GNRI), the prognostic nutritional index (PNI), and the controlling nutritional status (CONUT). AKI was determined using the KDIGO criteria. The authors performed logistic regression analysis to investigate the association between preoperative nutritional status and postoperative AKI, as well as the predictive value of nutritional scores for postoperative AKI. Results: A total of 2775 patients were included in the study, of which 707 (25.5%), 291 (10.5%), and 517 (18.6%) had moderate to severe malnutrition according to GNRI, PNI, and CONUT calculations. After surgery, 144 (5.2%) patients developed AKI, 86.1% at stage 1, 11.1% at stage 2, and 2.8% at stage 3 as determined by KDIGO criteria. After adjustment for traditional risk factors, worse nutritional scores were associated with a higher AKI risk. In addition to traditional risk factors, these nutritional indices improved the predictive ability of AKI prediction models, as demonstrated by significant improvements in integrated discrimination and net reclassification. Conclusions: Poor preoperative nutritional status, as assessed by GNRI, PNI, and CONUT scores, was associated with an increased risk of postoperative AKI. Incorporating these scores into AKI prediction models improved their performance. These findings emphasize the need for screening surgical patients for malnutrition risk. Further research is needed to determine whether preoperative malnutrition assessment and intervention can reduce postoperative AKI incidence.

Sleep plays an essential role in all studied animals with a nervous system. However, sleep deprivation leads to various pathological changes and neurobehavioral problems. Astrocytes are the most abundant cells in the brain and are involved in various important functions, including neurotransmitter and ion homeostasis, synaptic and neuronal modulation, and blood–brain barrier maintenance; furthermore, they are associated with numerous neurodegenerative diseases, pain, and mood disorders. Moreover, astrocytes are increasingly being recognized as vital contributors to the regulation of sleep-wake cycles, both locally and in specific neural circuits. In this review, we begin by describing the role of astrocytes in regulating sleep and circadian rhythms, focusing on: (i) neuronal activity; (ii) metabolism; (iii) the glymphatic system; (iv) neuroinflammation; and (v) astrocyte–microglia cross-talk. Moreover, we review the role of astrocytes in sleep deprivation comorbidities and sleep deprivation-related brain disorders. Finally, we discuss potential interventions targeting astrocytes to prevent or treat sleep deprivation-related brain disorders. Pursuing these questions would pave the way for a deeper understanding of the cellular and neural mechanisms underlying sleep deprivation-comorbid brain disorders.

Background: Early identification of patients at high-risk of postoperative acute kidney injury (AKI) can facilitate the development of preventive approaches. This study aimed to develop prediction models for postoperative AKI in noncardiac surgery using machine learning algorithms. The authors also evaluated the predictive performance of models that included only preoperative variables or only important predictors. Materials and methods: Adult patients undergoing noncardiac surgery were retrospectively included in the study (76 457 patients in the discovery cohort and 11 910 patients in the validation cohort). AKI was determined using the KDIGO criteria. The prediction model was developed using 87 variables (56 preoperative variables and 31 intraoperative variables). A variety of machine learning algorithms were employed to develop the model, including logistic regression, random forest, extreme gradient boosting, and gradient boosting decision trees. The performance of different models was compared using the area under the receiver operating characteristic curve (AUROC). Shapley Additive Explanations (SHAP) analysis was employed for model interpretation. Results: The patients in the discovery cohort had a median age of 52 years (IQR: 42–61 years), and 1179 patients (1.5%) developed AKI after surgery. The gradient boosting decision trees algorithm showed the best predictive performance using all available variables, or only preoperative variables. The AUROCs were 0.849 (95% CI: 0.835–0.863) and 0.828 (95% CI: 0.813–0.843), respectively. The SHAP analysis showed that age, surgical duration, preoperative serum creatinine, and gamma-glutamyltransferase, as well as American Society of Anesthesiologists physical status III were the most important five features. When gradually reducing the features, the AUROCs decreased from 0.852 (including the top 40 features) to 0.839 (including the top 10 features). In the validation cohort, the authors observed a similar pattern regarding the models’ predictive performance. Conclusions: The machine learning models the authors developed had satisfactory predictive performance for identifying high-risk postoperative AKI patients. Furthermore, the authors found that model performance was only slightly affected when only preoperative variables or only the most important predictive features were included.

Background: Early identification of high-risk factors for inadequate analgesia and adverse reactions in obstetric patients is critical for improving outcomes. This study developed a machine learning model to predict these factors and optimize anesthesia management in obstetric surgery. Material and methods: This prospective study included 763 obstetric patients who underwent elective cesarean delivery between December 2023 and May 2024. A machine-learning model for postoperative analgesia and adverse reactions (MLPIAAR) was constructed using 42 variables categorized into preoperative (18), intraoperative (12), and postoperative (12) factors. Ten algorithms were applied for model development. Model performance was optimized through 10-fold cross-validation and GridSearchCV. Evaluation metrics included receiver operating characteristic, area under the curve, accuracy, and precision. Model interpretability was enhanced using SHapley Additive exPlanations (SHAP). Regression analysis explored PCA compression frequency, consumption, and satisfaction of PCA factors. Validation was performed using predicted values and scatter plots. Results: Our study found 24.25% of patients experienced inadequate postoperative analgesia within 24 h. Postoperative nausea also occurred in 23.20% of patients. Simultaneously using dexamethasone and flurbiprofen axetil reduced both risks. However, pregnancy-induced hypertension and intraoperative shivering increased nausea risk. Postoperative vomiting occurred in 11.80% of patients, primarily associated with intravenous PCA type. Hydromorphone PCA showed a higher vomiting incidence than nalbuphine PCA. Intraoperative nausea and vomiting increased postoperative vomiting risk. Patients receiving diclofenac sodium and those with longer surgeries exhibited higher PCA compression frequencies and consumption. Higher preoperative Edinburgh Postnatal Depression Scale scores correlated with increased PCA consumption. Conclusions: The MLPIAAR model predicted high-risk factors for inadequate analgesia, nausea, and vomiting in obstetric patients. Dexamethasone and flurbiprofen axetil reduced these risks, while hydromorphone PCA increased vomiting risk compared to nalbuphine PCA. Machine learning and SHAP are valuable for optimizing anesthesia and analgesia management in obstetrics.

Astrocytes, the most abundant cells in the brain, are integral to sleep regulation. In the context of a healthy neural environment, these glial cells exert a profound influence on the sleep-wake cycle, modulating both rapid eye movement (REM) and non-REM sleep phases. However, emerging literature underscores perturbations in astrocytic function as potential etiological factors in sleep disorders, either as protopathy or comorbidity. As known, sleep disorders significantly increase the risk of neurodegenerative, cardiovascular, metabolic, or psychiatric diseases. Meanwhile, sleep disorders are commonly screened as comorbidities in various neurodegenerative diseases, epilepsy, and others. Building on existing research that examines the role of astrocytes in sleep disorders, this review aims to elucidate the potential mechanisms by which astrocytes influence sleep regulation and contribute to sleep disorders in the varied settings of brain diseases. The review emphasizes the significance of astrocyte-mediated mechanisms in sleep disorders and their associated comorbidities, highlighting the need for further research.