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

Sun, Rao; Zhou, Zhiqiang; Li, Xinhua; Xu, Qiaoqiao; Zhou, Biyun; Yu, Honghui; Zhang, Wanjun; Sun, Qi; Zhang, Xiang; Luo, Xiaoxiao; Li, Shiyong; Luo, Ailin

doi:10.1097/js9.0000000000000861

Cited by 4 publications

(3 citation statements)

References 72 publications

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“…A recent study found preoperative fasting protected against intestinal ischemia/reperfusion injury by modulating gut microbiota [207]. Our recent study also showed that preoperative nutritional status could predict postoperative renal injury [208]. However, until now, there is no sufficient evidence on whether IF influences the outcome of PND.…”

Section: Perioperative Neurocognitive Dysfunction (Pnd)mentioning

confidence: 78%

Intermittent Fasting on Neurologic Diseases: Potential Role of Gut Microbiota

Guo,

Wang,

et al. 2023

Nutrients

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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.

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Section: Perioperative Neurocognitive Dysfunction (Pnd)mentioning

confidence: 78%

Intermittent Fasting on Neurologic Diseases: Potential Role of Gut Microbiota

Guo,

Wang,

et al. 2023

Nutrients

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“…However, serum creatinine levels may underestimate renal function impairment in populations with diminished muscle mass 41 , such as those suffering from sarcopenia or malnutrition. It has been demonstrated that these populations are at an increased risk of AKI 2 , 42 . In a recent study, a U-shaped relationship has been demonstrated between preoperative creatinine and postoperative AKI 43 .…”

Section: Discussionmentioning

confidence: 99%

“…Acute kidney injury (AKI) is a common surgical complication characterized by a rapid decline in renal function 1 , 2 . Patients with AKI are at an increased risk of developing chronic kidney disease and end-stage renal disease, which has been associated with an increased risk of morbidity, mortality, and financial burdens 3 – 5 .…”

Section: Introductionmentioning

confidence: 99%

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

Sun,

Li,

Wei

et al. 2024

International Journal of Surgery

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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. We 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 (GBDT). 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 y), and 1179 patients (1.5%) developed AKI after surgery. The GBDT 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, we observed a similar pattern regarding the models’ predictive performance. Conclusions: The machine learning models we developed had satisfactory predictive performance for identifying high-risk postoperative AKI patients. Further, we found that model performance was only slightly affected when only preoperative variables or only the most important predictive features were included.

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