ObjectivesIn recent years, enhanced recovery after surgery (ERAS) has been widely used in the field of urology, especially in radical cystectomy and radical prostatectomy, and has demonstrated its advantages. Although studies on the application of ERAS in partial nephrectomy for renal tumors are increasing, the conclusions are mixed, especially in terms of postoperative complications, etc, and its safety and efficacy are questionable. We conducted a systematic review and meta-analysis to assess the safety and efficacy of ERAS in the application of partial nephrectomy for renal tumors.MethodsPubmed, Embase, Cohrance library, Web of science and Chinese databases (CNKI, VIP, Wangfang and CBM) were systematically searched for all published literature related to the application of enhanced recovery after surgery in partial nephrectomy for renal tumors from the date of establishment to July 15, 2022, and the literature was screened by inclusion/exclusion criteria. The quality of the literature was evaluated for each of the included literature. This Meta-analysis was registered on PROSPERO (CRD42022351038) and data were processed using Review Manager 5.4 and Stata 16.0SE. The results were presented and analyzed by weighted mean difference (WMD), Standard Mean Difference (SMD) and risk ratio (RR) at their 95% confidence interval (CI). Finally, the limitations of this study are analyzed in order to provide a more objective view of the results of this study.ResultsThis meta-analysis included 35 literature, including 19 retrospective cohort studies and 16 randomized controlled studies with a total of 3171 patients. The ERAS group was found to exhibit advantages in the following outcome indicators: postoperative hospital stay (WMD=-2.88, 95% CI: -3.71 to -2.05, p<0.001), total hospital stay (WMD=-3.35, 95% CI: -3.73 to -2.97, p<0.001), time to first postoperative bed activity (SMD=-3.80, 95% CI: -4.61 to -2.98, p < 0.001), time to first postoperative anal exhaust (SMD=-1.55, 95% CI: -1.92 to -1.18, p < 0.001), time to first postoperative bowel movement (SMD=-1.52, 95% CI: -2.08 to -0.96, p < 0.001), time to first postoperative food intake (SMD=-3.65, 95% CI: -4.59 to -2.71, p<0.001), time to catheter removal (SMD=-3.69, 95% CI: -4.61 to -2.77, p<0.001), time to drainage tube removal (SMD=-2.77, 95% CI: -3.41 to -2.13, p<0.001), total postoperative complication incidence (RR=0.41, 95% CI: 0.35 to 0.49, p<0.001), postoperative hemorrhage incidence (RR=0.41, 95% CI: 0.26 to 0.66, p<0.001), postoperative urinary leakage incidence (RR=0.27, 95% CI: 0.11 to 0.65, p=0.004), deep vein thrombosis incidence (RR=0.14, 95% CI: 0.06 to 0.36, p<0.001), and hospitalization costs (WMD=-0.82, 95% CI: -1.20 to -0.43, p<0.001).ConclusionERAS is safe and effective in partial nephrectomy of renal tumors. In addition, ERAS can improve the turnover rate of hospital beds, reduce medical costs and improve the utilization rate of medical resources.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO, identifier CRD42022351038.
Urolithiasis is a common and frequent disease in urology. Percutaneous nephrolithotomy (PCNL) is preferred for the treatment of upper urinary tract stones and complicated renal stones >2 cm in diameter, but it has a higher rate of postoperative complications, especially infection, compared with other minimally invasive treatments for urinary stones. Complications associated with infection after percutaneous nephrolithotomy include transient fever, systemic inflammatory response syndrome (SIRS), and sepsis, which is considered one of the most common causes of perioperative death after percutaneous nephrolithotomy. In contrast, SIRS serves as a sentinel for sepsis, so early intervention of SIRS by biomarker identification can reduce the incidence of postoperative sepsis, which in turn reduces the length of stay and hospital costs for patients. In this paper, we summarize traditional inflammatory indicators, novel inflammatory indicators, composite inflammatory indicators and other biomarkers for early identification of systemic inflammatory response syndrome after percutaneous nephrolithotomy.
Purpose Application of machine learning in bone metastasis of prostate cancer based on inflammation and nutritional indicators. Methods Retrospective analysis the clinical data of patients with prostate cancer initially diagnosed in the Department of Urology of Gansu Provincial People's Hospital from June 2017 to June 2022. Logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) are used to jointly screened the model features. The filtered features are incorporated into algorithms including LR, random forest (RF), extreme gradient boosting (XGBoost), naive nayes (NB), k-nearest neighbor (KNN), and decision tree (DT), to develop prostate cancer bone metastasis models. Results A total of 404 patients were finally screened. Gleason score, T stage, N stage, PSA and ALP were used as features for modeling. The average AUC of the 5-fold cross-validation for each machine learning model in the training set is: LR (AUC = 0.9054), RF (AUC = 0.9032), NB (AUC = 0.8961), KNN (AUC = 0.8704), DT (AUC = 0.8526), XGBoost (AUC = 0.8066). The AUC of each machine learning model in the test set is KNN (AUC = 0.9390, 95%CI: 0.8760 ~ 1), RF (AUC = 0.9290, 95%CI: 0.8718 ~ 0.9861), NB (AUC = 0.9268, 95%CI: 0.8615 ~ 0.9920), LR (AUC = 0.9212, 95%CI: 0.8506 ~ 0.9917), XGBoost (AUC = 0.8292, 95%CI: 0.7442 ~ 0.9141), DT (AUC = 0.8057, 95%CI: 0.7100 ~ 0.9014). A comprehensive evaluation of the DeLong test among different models and each evaluation metric shows that KNN is the best machine learning model in the study. Conclusion A bone metastasis model of prostate cancer was established, and it was observed that indicators such as inflammation and nutrition had a weak correlation with bone metastasis.
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