Prognostic significance of preoperative nutritional status for postoperative acute kidney injury in older patients undergoing major abdominal surgery: a retrospective cohort study

Nutritional Indices Predict All Cause Mortality in Patients with Multi-/Rifampicin-Drug Resistant Tuberculosis

Background

Multidrug- and rifampicin-resistant tuberculosis (MDR/RR-TB) with high mortality remains a public health crisis and health security threat. This study aimed to explore the predictive value of nutritional indices for all-cause mortality (ACM) in MDR/RR-TB patients.

Methods

We retrospectively recruited MDR/RR-TB patients between January 2015 and December 2021, randomly assigning them to training and validation cohorts. Patients were divided into high nutritional risk groups (HNRGs) and low nutritional risk groups (LNRGs) based on the optimal cut-off value obtained from receiver operating characteristic (ROC) analyses of the hemoglobin-albumin-lymphocyte-platelet (HALP) score, prognostic nutritional index (PNI), and controlling nutritional status (CONUT) score. In the training cohort, Kaplan-Meier survival curves and Log rank tests were used to compare overall survival (OS) between the groups. Cox risk proportion regression analyses were used to explore the risk factors of ACM in patients with MDR/RR-TB. The predictive performance of ACM was assessed using area under the curve (AUC), sensitivity and specificity of ROC analyses.

Results

A total of 524 MDR/RR-TB patients, with 255 in the training cohort and 269 in the validation cohort, were included. Survival analyses in the training cohort revealed significantly lower OS in the HNRGs compared to the LNRGs. After adjusting for covariates, multivariate analysis identified low HALP score, low PNI and high CONUT score were independent risk factors for ACM in MDR/RR-TB patients. ROC analyses demonstrated good predictive performance for ACM with AUCs of 0.765, 0.783, 0.807, and 0.811 for HALP score, PNI, CONUT score, and their combination, respectively. Similar results were observed in the validation set.

Conclusion

HALP score, PNI, and CONUT scores could effectively predict ACM in patients with MDR/RR-TB. Hence, routine screening for malnutrition should be given more attention in clinical practice to identify MDR/RR-TB patients at higher risk of mortality and provide them with nutritional support to reduce mortality.

Development of interpretable machine learning models for prediction of acute kidney injury after noncardiac surgery: a retrospective cohort study

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.

Intermittent Fasting on Neurologic Diseases: Potential Role of Gut Microbiota

As the global population ages, the prevalence of neurodegenerative diseases is surging. These disorders have a multifaceted pathogenesis, entwined with genetic and environmental factors. Emerging research underscores the profound influence of diet on the development and progression of health conditions. Intermittent fasting (IF), a dietary pattern that is increasingly embraced and recommended, has demonstrated potential in improving neurophysiological functions and mitigating pathological injuries with few adverse effects. Although the precise mechanisms of IF's beneficial impact are not yet completely understood, gut microbiota and their metabolites are believed to be pivotal in mediating these effects. This review endeavors to thoroughly examine current studies on the shifts in gut microbiota and metabolite profiles prompted by IF, and their possible consequences for neural health. It also highlights the significance of dietary strategies as a clinical consideration for those with neurological conditions.