Accurate prediction of postoperative mortality is important for not only successful postoperative patient care but also for information-based shared decision-making with patients and efficient allocation of medical resources. This study aimed to create a machine-learning prediction model for 30-day mortality after a non-cardiac surgery that adapts to the manageable amount of clinical information as input features and is validated against multi-centered rather than single-centered data. Data were collected from 454,404 patients over 18 years of age who underwent non-cardiac surgeries from four independent institutions. We performed a retrospective analysis of the retrieved data. Only 12–18 clinical variables were used for model training. Logistic regression, random forest classifier, extreme gradient boosting (XGBoost), and deep neural network methods were applied to compare the prediction performances. To reduce overfitting and create a robust model, bootstrapping and grid search with tenfold cross-validation were performed. The XGBoost method in Seoul National University Hospital (SNUH) data delivers the best performance in terms of the area under receiver operating characteristic curve (AUROC) (0.9376) and the area under the precision-recall curve (0.1593). The predictive performance was the best when the SNUH model was validated with Ewha Womans University Medical Center data (AUROC, 0.941). Preoperative albumin, prothrombin time, and age were the most important features in the model for each hospital. It is possible to create a robust artificial intelligence prediction model applicable to multiple institutions through a light predictive model using only minimal preoperative information that can be automatically extracted from each hospital.
Purpose To establish a prospective, comprehensive, multidisciplinary, bio-bank linked genitourinary cancer cohort based on standard real practice. Materials and Methods We established the Seoul National University Prospectively Enrolled Registry for Genitourinary Cancer (SUPER-GUC), a prospective cohort clinical database and bio-specimen repository system for prostate cancer (SUPER-PC), renal cell carcinoma (SUPER-RCC), and urothelial cancer (SUPER-UC) at a high-volume, tertiary institution. Each cohort consists of several sub-cohorts based on treatment or disease status. Detailed longitudinal clinical information, and general and disease specific patient-reported outcomes are captured. We use the same evaluation format and questionnaires for all participating departments. Patients' blood, urine, tumor, and normal tissues are collected. The number of registered patients and their basic characteristics are summarized. For the surgical sub-cohort, study participation, bio-specimen, and tissue banking rates are analyzed. Results Since March 2016, 11 sub-cohorts for all disease statuses have been opened, ranging from low-risk localized to metastatic disease. SUPER-PC, SUPER-RCC, and SUPER-UC enrolled 929, 796, and 1,221 patients, respectively. Study participation, bio-sampling, and fresh frozen tumor banking rates of surgical sub-cohorts were 89.0% to 93.1%, 91.2% to 99.1%, and 56.9% to 79.1%, respectively. Conclusions SUPER-GUC is a study platform for comparative outcome, quality-of-life, and translational (genetics, biomarkers) research for genitourinary cancer.
Purpose: We performed a study-level meta-analysis to summarize the current evidence on the correlation between pretreatment neutrophil-to-lymphocyte ratios (NLR) and oncological outcomes in each type of management for urothelial carcinoma. Method: All articles published until February 2017 in PubMed, Scopus, and EMBASE database were collected and reviewed. The current evidence on correlations between pretreatment NLR and oncological outcomes in each type of management for urothelial carcinoma, including transurethral resection of bladder tumor (TURBT), radical cystectomy (RCx), chemotherapy (CTx), and nephroureterectomy (NUx), were summarized. Results: Thirty-eight studies containing clinical information on 16,379 patients were analyzed in this study. Pooled hazard ratios (HR) and odds ratios (OR) with 95% confidence intervals were calculated after weighing each study. Heterogeneity among the studies and publication bias were assessed. Pretreatment NLR was significantly associated with muscle invasiveness (OR: 4.27), recurrence free survival (RFS, HR: 2.32), and progression-free survival (PFS, HR: 2.45) in TURBT patients. In the RCx patients, high NLR was negatively associated with both disease status (extravesical extension and lymph-node positivity, OR: 1.14 and 1.43, respectively) and oncological outcomes [overall survival (OS), PFS], and cancer specific survival (CSS, HR: 1.18, 1.12, and 1.35, respectively). Pretreatment NLR was negatively correlated with pathologic downstaging (OR: 0.79) and positively correlated with PFS (HR: 1.30) and OS (HR: 1.44) in CTx patients. For patients who underwent NUx, pretreatment NLR was significantly associated with OS (HR: 1.72), PFS (HR: 1.63), and CSS (HR: 1.68). Conclusions: Pretreatment NLR is a useful biomarker for disease aggressiveness, oncological outcome, and treatment response in the management of patients with urothelial carcinoma. More evidence is needed to clarify these results.
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